Embed Size (px)
Citation preview
Effect of Occupational Health, Safety & Welfare Measures on
Employee Performance with Mediation of Job Satisfaction
(A Survey of Sugar Mills Employees in KP, Pakistan)
Ph.D Thesis
By
Iftikhar Ahmad Khan
Registration No. 418-DPA-05
A thesis is submitted in partial fulfillment of the requirements for
the degree of PhD in Management Studies
Department of Public Administration
Gomal University D. I. Khan
KP, PAKISTAN
November, 2019
ii
CERTIFICATE OF APPROVAL FROM THE SUPERVISORY
COMMITTEE
We, the Departmental Supervisory Committee, hereby certify that the contents and form of
dissertation submitted by Iftikhar Ahmad Khan, candidate for PhD, Department of Public
Administration, Gomal University, Dera Ismail Khan were checked and found satisfactory. As per
directions of the Higher Education Commission, the thesis of the student was checked for
plagiarism in which Plagiarism 13% similarities were found as per report attached hereto which is
within the acceptable range. Thus, the revised thesis is submitted for notification.
Supervisory Committee
Name
a) Dr. Abdul Sattar_____________Supervisor (from the major field)
b) Nil________________________ Co-Supervisor (if any)
c) Prof. Dr Shadiullah Khan______ Member (from the major field)
d) Dr. Qamar Afaq Qureshi________ Member (from the minor field)
Forwarded by
Professor Dr Shadiullah Khan_______ Chairperson/Director
Dean Faculty of Arts ________
iii
Dedication I dedicate this effort to my parents, my sibs, my wife and two kids, Ayesha & Hamza who had to
bear with me long hours of hard work. I owe a great debt of gratitude to all of them.
iv
List of Contents
Description Page No
Title………………………………………………………………. i
Certificate ……………………………………………………….. Ii
Dedication………………………………………………………. Iii
List of Contents…………………………………………………. iv
Student’s Declaration………………………………………………. Vii
List of Tables………………………………………………………. Viii
List of Figures……………………………………………………… Xi
List of Abbreviations………………………………………………. Xii
List of Appendices…………………………………………………. Xiii
Acknowledgement…………………………………………………. Xiv
Abstract…………………………………………………………….. Xvi
Chapter 1: Introduction………………………………………….. 1
1
4
5
5
5
6
1.1 Background
1.2 Statement of the Problem
1.3 Objectives of the Study
1.4 Limitations and Delimitations of the Study
1.51.
6
Significance of the Study
Organization of the Thesis
Chapter 2: Review of Literature
2.1 Existing Research
2.1.1 Workplace Hazards
2.1.2 Definitions of Occupational Health & Safety (OHS)
2.1.3 Principles & Practices of OHS
2.1.4 OHS Legislation
2.1.5 Issues of OHS in Developing versus Developed World
2.1.6 Issues of OHS of Sugar Mills in KP, Pakistan
2.1.7 Health Measures
2.1.8 Safety Measures
2.1.9 Welfare Measures
2.1.10 Job Satisfaction
2.1.11 Dimensions of Job Satisfaction
2.1.12 Employee Performance
2.1.13 Dimensions of Employee Performance
2.1.14 Theories Guiding this Research
2.1.15 Concepts Searched in Literature
2.2 Conceptual Framework
8
8
9
11
12
13
15
16
17
18
20
22
23
25
27
29
32
34
v
2.3 List of Hypotheses 35
Chapter 3: Materials and Methods 37
3.1 Research Philosophy
3.2 Approach
3.3 The Population and Sampling design
3.4 The Pilot study (n=36)
3.5 Sample size
3.6 Sampling Technique
3.7 Data Collection Methods
3.7.1 Literature Survey
3.7.2 Field Survey
3.8 Data Analysis Tools
3.8.1 Qualitative Data Analysis
3.8.2 Quantitative Data Analysis
3.9 List of Working Concepts
3.10 Operationalization of the Concepts
3.11 Reliability
3.12 Validity
3.13 Ethical Considerations
37
38
38
39
39
40
41
41
42
43
43
44
46
47
49
51
52
Chapter 4: Results & Discussion
4.1 Data Preparation for Analysis
4.1.1 Editing and Missing responses.
4.1.2 Data Coding
4.1.3 Categorization and Data Transformation
4.1.4 Outliers
4.1.5 Adequacy of fit
4.2 Validity Analysis
4.3 Reliability Analysis
4.4 Descriptive Analysis
4.5 Inferential statistics (Testing of hypothesis)
4.5.1 Correlation Analysis
4.5.2 Regression Analysis
4.5.3 Mediation Analysis
4.5.4 Tests of Significance
4.6 Discussion
4.6.1 Restatement of the objectives
4.6.2 Materials and Methods
53
53
53
53
53
54
54
59
70
73
75
75
76
79
84
91
91
92
Chapter 5: Conclusion and Recommendations
5.1 Summary of Results
99
99
vi
5.2 Conclusions
5.3 Recommendations for Practice
5.4 Policy Implications
5.5 Practical Implications
5.6 Future Research Directions
103
103
105
105
106
Chapter 6: References 108
vii
Student’s Declaration
I, Iftikhar Ahmad Khan, PhD scholar of the Department of Public Administration, Gomal
University D.I.Khan, do hereby state that my Ph.D. thesis titled ‘Effect of Occupational Health,
Safety & Welfare Measures on Employee Performance with Mediation of Job Satisfaction (A
Survey of Sugar Mills Employees in KP, Pakistan)’ is my own work and has not been submitted
previously by me for taking any degree from Gomal University, Dera Ismail Khan or anywhere
else in the country/world.
I understand the zero tolerance policy of the HEC and Gomal University, Dera Ismail Khan
towards plagiarism. Therefore I declare that no portion of my thesis has been plagiarized and any
material used as reference is properly cited. I undertake that if I am found guilty of any formal
plagiarism in the above titled thesis even after award of PhD degree, the university reserves the
rights to withdraw/revoke my PhD degree and that HEC has the right to publish my name on the
website on which names of students are placed who submitted plagiarized work.
This research work has been conducted by me under the supervision of my supervisor Dr. Abdul
Sattar, Associate Professor, Department of Public Health Administration, Gomal University, Dera
Ismail Khan and that to the best of my knowledge and belief it does not restrain any material
previously published or any material in the past submitted for a degree in any University. The
candidate confirms that the work submitted is his own and the appropriate credit has been given
to the work of the others.
Name of Student Iftikhar Ahmad Khan_____________ Date___________
Name of Supervisor Dr. Abdul Sattar ______________ Date___________
viii
List of Tables
Table 3.1: Computation of Sample Size .…………………………………………………… 39
Table 3.2: Proportionate Stratified Sampling …………………….………………………… 41
Table 3.3: List of the extracted research variables along with definitions……...…………. 46
Table 3.4: List of the extracted demographic variables along with definitions……………. 46
Table 3.5 Operationalization of Research Variable…………………………………………. 47
Table 3.6 List of Demographic Variables…………………………………………………… 49
Table 4.1: Skewness and Kurtosis Statistics (n=263)……………………………………….. 55
Table 4.2: Statistics of the Distribution of the HM in Sugar Mills of KP, Pakistan………… 55
Table 4.3: Statistics of the Distribution of the SM in Sugar Mills of KP, Pakistan………… 55
Table 4.4: Statistics of the Distribution of the WM in Sugar Mills of KP, Pakistan……….. 56
Table 4.5: Statistics of the Distribution of the JS in Sugar Mills of KP, Pakistan …………. 56
Table 4.6: Statistics of the Distribution of the EP in Sugar Mills of KP, Pakistan…..…...... 56
Table 4.7: KMO and Bartlett’s Test for HM………………………..……………………….. 59
Table 4.7 a: Communalities…………………………………………………………………… 59
Table 4.7 b: Total Variance Explained ………………………………………………………. 60
Table 4.7 c: Component Matrix………………………………………………………………. 60
Table: 4.8 KMO and Bartlett’s Test for SM……………………………………….………… 61
Table 4.8 a: Communalities………………………………………………………………….. 61
Table 4.8 b: Total Variance Explained ………………………………………………………. 61
Table 4.8c: Component Matrix ………………………………………………………………. 62
Table: 4.9 KMO and Bartlett’s Test for WM………………………………………………… 62
Table 4.9 a: Communalities………………………………………………………………….. 62
Table 4.9 b: Total Variance Explained ………………………………………………………. 63
Table 4.9 c: Component Matrix ……………………………………………………………… 63
Table 4.10: KMO and Bartlett’s Test for JS……………………………………………......... 64
Table 4.10 a: Communalities…………………………………………………………………. 64
Table 4.10 b: Total Variance Explained …………………………………………………….. 64
Table 4.10 c: Component Matrix ……………………………………………………………. 65
Table: 4.11: KMO and Bartlett’s Test for EP……………………………………………….. 65
Table 4.11 a: Communalities………………………………………………………………… 66
Table 4.11 b: Total Variance Explained …………………………………………………….. 66
Table 4.11 c: Component Matrix…………………………………………………………….. 67
Table 4.12: Reliability Statistics for HM ……………………………………………………. 70
ix
Table 4.12a: Item total statistics for HM ……………………………………………………. 70
Table 4.13: Reliability Statistics for SM ……………………………………………………. 70
Table 4.13a: Item total Statistics for SM …………………………………………………… 70
Table 4.14: Reliability Statistics for WM ………………………………………………….. 70
Table 4.14a: Item total Statistics for WM ………………………………………………….. 71
Table 4.14b: Combined reliability for OHS ………………………………………………… 71
Table 4.15: Reliability Statistics for JS …………………………………………………….. 71
Table 4.15a: Item total Statistics for JS ……………………………………………………. 72
Table 4.16: Reliability Statistics for EP ……………………………………………………. 72
Table 4.16a: Item total Statistics for EP ……………………………………………………. 72
Table 4.17: Frequency Distribution of Age…………………………………………………. 73
Table 4.18: Frequency Distribution of Residence………………………………………….. 73
Table 4.19: Frequency Distribution of Education…………………………………………… 74
Table 4.20: Frequency Distribution of Experience…………………………………………. 74
Table 4.21: Descriptive Analysis of Research variables……………………………………. 74
Table 4.22: Correlation of EP with HM, SM, WM, and JS………………………………… 75
Table 4.23: Model Summary [H2]………………………………………………………….. 76
Table 4.23 a: ANOVA………………………………………………………………………. 76
Table 4.23 b: Coefficients of Regression……………………………………………………. 76
Table 4.24: Assumptions of Multiple Linear Regression……………………………………. 78
Table 4.25: Model Summary [H3]…………………………………………………………… 80
Table 4.25 a: ANOVA……………………………………………………………………….. 80
Table 4.25 b: Coefficients…………………………………………………………………… 80
Table 4.26: Model Summary………………………………………………………………… 81
Table 4.26 a: ANOVA……………………………………………………………………….. 82
Table 4.26 b: Coefficients……………………………………………………………………. 82
Table 4.27: Model Summary ………………………………………………………………… 83
Table 4.27 a: ANOVA……………………………………………………………………….. 83
Table 4.27 b: Coefficients……………………………………………………………………. 83
Table 4.28: Group Statistics for Age…………………………………………………………. 84
Table 4.28 a: t-Test Results, Age on all five Research Variables……….…………………… 85
Table 4.29: Group Statistics for Residence…………………………………………………… 85
Table 4.29 a: t-Test Results, Residence on all five Research Variables……………………… 85
Table 4.30: Group Statistic on Education…………………………………………………….. 86
x
Table 4.30a: One way ANOVA Education Groups vs. all five research variables ………….. 87
Table 4.30b: Tukey HSD Results of Multiple Comparisons of Different Groups……………. 88
Table 4.31: Group Statistics on Experience …………………………………………………. 89
Table 4.31 a: t-Test Results, Experience on all five Research Variables……..………………. 90
Table 4.32 Correlation Summary ………………………………………….………………….. 92
Table 4.33 Regression Summary ……………………………………………………………… 94
Table 4.34 Mediational Summary………………………………………...…………………… 95
Table 4.35 Tests of Significance Summary………………………………..………………….. 96
Table 5.1 Correlation Summary ………………………………………….…………………… 99
Table 5.2 Regression Summary ……………………………………………………………….. 99
Table 5.3 Mediational Summary………………………………………...…………………….. 100
Table 5.4 Tests of Significance Summary………………………………..…………………… 101
Table 5.5 Summary of Statistical Test………………………………..…………….…………. 102
xi
List of Figures
Figure 2.1: Theoretical Frame Work…………………………………………………………… 35
Figure 3.1 Theoretical Network Approach to Qualitative Data Analysis …………………….. 44
Figure 3.2 Baron & Kenny (1986) Mediation-Model ……………………………… 46
Figure 4.1: Histogram of the Distribution of the HM of Employees in Sugar Mills of KP……. 60
Figure 4.2: Histogram of the Distribution of the SM of Employees in Sugar Mills of KP........ 60
Figure 4.3: Histogram of the Distribution of the WM of Employees in Sugar Mills of KP…… 61
Figure 4.4: Histogram of the Distribution of the JS of Employees in Sugar Mills of KP……… 62
Figure 4.5: Histogram of the Distribution of the EV of Employees in Sugar Mills of KP……. 62
Figure 4.6: Scree Plot for HM………………………………………………………………….. 59
Figure 4.7: Scree Plot of SM…………………………………………………………………… 61
Figure 4.8: Scree Plot for WM…………………………………………………………………. 63
Figure 4.9: Scree Plot for JS……………………………………………………………………. 65
Figure 4.10: Scree Plot for EP………………………………………………………………….. 67
Figure 4.11: Normal P-P Plot of Regression Standardized Residual………………………….. 79
Figure 4.12: Mediation Mode l ………………………………………………………………… 80
Figure 4.13: Mediation Model 2………………………………………………………………... 84
Figure 4.14: Mediation Model 3………………………………………………………………… 87
xii
LIST OF ABBREVIATIONS
ANOVA Analysis of variance
CFA Confirmatory Factor Analysis
DF Degree of freedom
DV Dependent Variable
FA Factor analysis
GDP Gross Domestic Product
IV Independent Variable
JS Job Satisfaction
KMO Kaiser Meyer-Olkin
OHS Occupational Health & Safety
R Correlation Coefficient
R2 Coefficient of Determination
SD Standard Deviation
SE Standard Error
Sig Significant
SPSS Statistical Package for Social Sciences
TFW Theoretical framework
CFW Conceptual framework
TOS Test of significance
EVA Equal Variances Assumed
EVNA Equal Variances Not Assumed
xiii
List of Appendices
1. Questionnaire 122
xiv
ACKNOWLEDGEMENTS
I owe tremendous respect and gratitude to my parents, family, relatives and friends who have been
instrumental in one way or the other in getting me to the place where I have been right now.
First of all, thanks to Almighty Allah who bestowed me with the vitalities to conduct this research
effectively and to the satisfaction of my supervisor. Research is a collective activity where
galaxies of honorable persons help and assist the researcher at different levels of the research-
trajectory. Finalizing this study has been a challenge but inspiring experience.
I owe an immense debt of gratitude to my supervisor, Dr. Abdus Sattar, Associate Professor,
Institute of Political and Administrative Studies, Gomal University, D.I.Khan who had always
been available for guidance. I also thank my mentor, Professor Dr. Allah Nawaz, Institute of
Political and Administrative Studies, Gomal University, D.I.Khan whose untiring support and
step-by-step guidance enabled me across the research activities from inception to finalization of
this research thesis. I am thankful to him for spending his valuable time in discussing and sharing
views on this project.
I am also indebted to Professor Dr. Shadiullah Khan, Director Institute of Political and
Administrative Studies and Dean faculty of Arts. I am thankful to my friends, Dr. Qamar Afaq
Qureshi, Mr. Muhammad Siddique, both from Institute of Political and Administrative Studies,
and Mr. Irfanullah Khan, Gomal University, D.I.Khan. Besides Dr. Yasir Hayat Mughal,
Assistant Professor, Department of Public Administration, Qurtaba University, D.I.Khan and Dr.
FaqirSajjad, Assistant Proferssor, Department of Public Administration, Khushal Khan Khattak
University, Karak, extended full cooperation and support during my work.
I would like to convey my profound sense of gratitude to my friend, Dr. Muhammad Marwat,
Assistant Professor, Department of Ophthalmology, Gomal Medical College, D.I.Khan to be with
me at every step of research trajectory.
I can’t overlook the contributions of the support staff of the Department of Public Administration.
I am also thankful to Mr. Umar Yamin & Misbah, my editorial assistants in the office of Chief
xv
Editor, Gomal journal of Medical Sciences, GMC, who worked hard on formatting of this report.
I also appreciate the efforts of sugar mill employees in filling the questionnaires during my field
study.
I am thankful to my undergraduate medical students, who always remained a source of motivation
for me to improve my research knowledge and skill. I despite being a very busy person as
Professor/ Chairperson, Department of Community Medicine/ and Chief Editor GJMS, Gomal
Medical College, D.I.Khan, Khyber Pakhtunkhwa, Pakistan missed no opportunity to teach them
and to learn from them.
Iftikhar Ahmad Khan
Candidate for Ph.D Degree in Management Studies
Dept. Public Administration,
Institute of Political and Administrative Studies,
Gomal University, DIK, KP, Pakistan
xvi
Effect of Occupational Health, Safety & Welfare Measures on
Employee Performance with Mediation of Job Satisfaction
(A Survey of Sugar Mills Employees in KP, Pakistan)
Abstract
According to literature, Employee performance (DV) depends on three important industrial
workplace variables; Health measures (IV), Safety measures (IV) and Welfare measures (IV),
whereas Job satisfaction (M) explains this relationship through a meditational role. No studies
could be found about perceptions of sugar mill workers of KP province of Pakistan. This
knowledge gap was our research problem. Research questions were; is there any significant
relationship between the DV & IVs? How far the relationship between criterion and predictors is
mediated by the mediator? Are there any significant demographic group mean differences of
research variables by age, residence, education and experience?
The objectives were to determine; the correlation and regression between the criterion &
predictors; The mediating role of the JS between the relationship of DV & IVs; Group mean
differences for subsamples based on four demographic variables. This survey was conducted at
six functional sugar mills of KP, from December, 2015 to March, 2016. Out of a target population
of 3956 employees, a sample of 263 was selected through proportionate stratified random
sampling technique. The four demographic variables being categorical were analyzed by
frequency and percentages. Five research variables being numeric were analyzed as mean & SD
through SPSS (V.20.0). Statistical techniques used were data normality, reliability, factor
analysis, correlation, regression & tests of significance.
There was statistically significant positive correlation between EP & Health measures, Safety
measures and Welfare measures. Regression model shows 40% of the variance in EP by the four
IVs (P=< 0.001). JS partially mediated the relationship between EP & predictors. Demographics
showed significant impacts on all the research variables, using t-test & ANOVA. The
recommendations for HRM include optimal standards of OHS. This will improve EP directly and
through better JS by capacity building of the employees on OHS.
1
Chapter 1: INTRODUCTION
The opening section of the thesis includes background of the problem, statement of the
problem, objectives of the study, limitations and delimitations, significance of study and the
organization of the thesis.
1.1 Background
The principal concern of current organizational management is to increase productivity of
their respective organizations. This is only possible through improvement in performance of
their employees. High performing individuals are therefore always deliberated as an asset.
Employees are considered the backbone of any manufacturing organization. Organizations
spend major resources and efforts to attract and retain actively involved employees. The
focus has now shifted from the financial performance to the nonfinancial factors as for
example quality controls and customer satisfaction of the organizations. In short, improving
EP has become tactically important with time and increasingly recognized worldwide as
organizational output depends on it (Al-Otaibi, Alharbi & Al-meleehan, 2015) and
Employee Performance (EP) is the variable of interest of this study which refers to
performing job tasks according to job description (Nawaz, et al., 2012).
Managers are constantly searching out factors to improve EP. Organizations have both legal
and moral obligations to provide healthy and safe work environments. The welfare of
employees as well as their families is the responsibility of their employers. Unsafe and
unhealthy atmosphere at work may adversely affect the physical, mental and social well-
being of the employees, resulting in significant loss to employee performance affecting
workers, employers, organizations, and countries (Ahmed & Shaukat, 2018). According to
literature, different health, safety, welfare interventions are considered relevant to improve
EP directly as well as through job satisfaction indirectly (Ajala, 2012). Health measures
(HM), Safety measures (SM) Welfare measures (WM) and Job Satisfaction (JS) are the four
factors tested to determine EP. Furthermore, JS role as mediator b/w predictors & criterion
along with demographic group mean differences of employees have also been tested
(Womoh, Owusu & Addo, 2013)
2
According to existing research, there is a range of physical, chemical, biological, mechanical/
ergonomics and psycho-social hazards the sugar mill workers are exposed to worldwide,
especially in developing countries including Pakistan. Sugar mills in Khyber Pakhtunkhwa
(KP) province of Pakistan which is one of the four provinces, located along the international
border with Afghanistan, are no exception to this. Sugar mills in KP, have hazardous
environments as mostly low paid, untrained employees work under substandard, unregulated
conditions. Problems in predictors are known to adversely affect EP. A great deal of research
about impact of these predictors on EP with JS as mediator is explained in literature. The
hazardous work atmosphere needs prevention & control. This may be possible by providing
health, safety, welfare measures (WM) and undertaking all interventions for job satisfaction
(JS) of sugar mill workers (Sattar & Shadiullah, 2011).
According to joint ILO/ WHO Committee for Global strategy on occupational health for all,
health, safety and welfare measures collectively taken are called occupational health & safety
(OHS). OHS aims to promote the highest degree of health, prevent from occupational
diseases, protect from accidents and injuries and placement of employees in all occupations
in favorable environment. OHS means mutual adaptation between an employee and his/ her
occupational setting (ILO, 1985). It serves as a dynamic equilibrium between employees and
their occupational setting. Like Preventive medicine, OHS uses epidemiology, biostatistics
and research as tools to provide comprehensive healthcare to workers in all professions.
OHS, being a preventive medicine, works at primary, secondary and tertiary levels of
prevention. Primary prevention is the combination of health promotion and specific
protection. Secondary prevention includes screening for chronic diseases and risk factors for
early diagnosis and prompt treatment by the occupational physician. Once disease has
occurred, options available are disability limitation and rehabilitation, both included in
tertiary prevention (Gyensare, Anku-Tsede & Kumedzro, 2018).
OHS covers all occupations. Industrial health is therefore a component of OHS. It is
extremely important to recognize negative effect of work setting on wellbeing of employees.
Research says that health, safety and welfare measures implemented optimally will have less
number of workplace accidents and diseases, less absenteeism among workers and lower
health insurance costs. Job facilities in accordance with OSH practices, improve morale and
job satisfaction, resulting in better performance by the employees. Safety and health practices
3
in industries increase productivity and profitability of organization (Nbirye, 2010; Malik et
al., 2011; Yusuf, Anis & Novita, 2012; Womoh, Owusu & Addo, 2013).
There is large incidence and prevalence of occupational morbidity, disability, and mortality
with disastrous consequences for individual, family, society, and employer. Loss through sick
pays, compensations and poor organizational reputation are the organizational costs. About
340 million occupational accidents, 160 million occupational illnesses and around 2.3 million
workers deaths per year, amounting to 6000 deaths daily and astounding $1.25 trillion costing
approximately 4% of world’s GDP annually. In every 15 seconds, 153 occupational accidents
and one death occur. Organizations need high performing employees to meet their goals, to
deliver excellent services and to achieve competitive advantage (ILO/ WHO global
estimates).
The Health measures (HM) are the primary, secondary and tertiary levels of prevention
activities regarding health and well-being of employees. Safety measures (SM) are concerned
with protection of employees, when cause and effect are closely related in time as for
example accidents and injuries. The WM are related to social benefits and welfare of
employees’ families for good standard of living such as income, housing, education, transport
and other basic facilities. The ultimate aim of all the three is to improve the overall quality of
their life (Khaqan, 2017). Job satisfaction (JS) is the subjective feeling of individuals
regarding liking/ disliking their jobs, which is determined by the fact whether the job caters
for their needs or not. Organizations demand performance from employees who in return
expect facilities for themselves and for their families. A worker satisfied from job is a happy
worker who in return pays back to the organization through high level of performance. EP is
the achievement of specific responsibilities measured against predefined standards of
correctness and completeness (Dwomoh, Owusu & Addo, 2013). The current study is about
the inter-relationship of the above mentioned five very important organizational workplace
variables in the context of sugar mill employees in Khyber Pakhtunkhwa (KP), Pakistan.
The interrelationships between SM, HM, WM, JS and EP have been extensively studied by
different researchers over the last few decades. EP is the dependent variable, whereas HM,
SM and WM are independent variables. JS is acting as the mediating or intervening variable
in the relationships between the predictors and criterion. Volumes of literature are about the
interrelationships among these variables as well as the role of JS as mediating variable in
4
research (Sattar & Shadiullah, 2011). In literature a number of assessment instruments have
been developed to frequently and periodically measure perceptions of employees about HM,
SM, WM, JS and EP. It is interesting to find out whether JS of the employees strengthens
(partial mediation) or totally disconnects the direct link between HM, SM & WM and EP
respectively (full mediation). Besides, whether the demographic attributes of the employees
impact their perceptions regarding above mentioned variables or not. We studied different
relevant theories in the existing literature working behind the interrelationships among our
variables of interest in the context of sugar mill workers. We ended up with our literature
survey in the form of theoretical model of inter relationships which was tested empirically in
the current study (Dhananjayan, & Ravichandran, 2018).
1.2 Statement of the problem
This study investigates that EP is determined by HM, SM, WM & JS. However, this
relationship is reportedly mediated by JS. Furthermore, demographic group mean differences
of employees have also been critical in determining EP, in the employees of sugar mills of
KP. Following are the research questions of this study.
1. Is there any statistically significant correlation between the EP and HM, SM, WM,
and JS respectively in sugar mills employees of KP?
2. Is there any statistically significant cause-n-effect relationship between the predictors
the EP (criterion) and HM, SM, WM, & JS (predictors)?
3. How far the relationship between EP (DV) and the HM, SM & WM (IVs)
respectively is mediated by the mediator?
4. Is there any role of Age, Residence, Education, and Experience (demographics) in
changing the responses of the employees about all five research variables; HM, SM,
WM, JS, and EP?
1.3 Objectives of the study
1. To measure correlations b/w EP with HM, SM, WM & JS
5
2. To compute cause & effect relationship b/w EP & HM, SM, WM & JS
3. To test the mediation of JS b/w EP & HM, SM, WM respectively
4. To compute demographic group mean differences of employees
1.4 Limitations and Delimitations of the study
Limitations are the factors out of control of the researcher. This study is based on reported
facts which may not present the actual conduct of the factory workers. The response rate was
also one of the limitations as it was not possible to get all questionnaires returned, therefore
some information might have been missed. The results might not be unbiased or fully
conclusive in giving a clear picture of the sugar mills as the management might have
influenced the free opinion of the workforce. The workers may be reluctant to release any
organizational information due to fear of victimization by the management. However,
reassurance by the researcher regarding confidentiality was accomplished to increase
attention level of workers. One of the limitations could be the limited number of variables
included in this research. Delimitations, on the other hand, are intentional such as financial
and time constraints behind small sample size and confining study to KP province only.
However, delimitations are non-damaging to research. These only confine it into limits.
1.5 Significance of the Study
Compliance of the recommendations of this first local version versus TFW model will
benefit;
1. HRM to understand the weightage of predictors to guide them to take measures about the
missing parts
2. Employees will have less injuries, accidents, illnesses and economic losses
3. Organizational output will be enhanced due to improved performance of employees &
profits &
4. Forthcoming researchers will find this research as a guideline to dig deeper into the issue
6
1.6 Organization of the thesis
The thesis comprises of five chapters
1. The introduction is the first chapter that presents the background, problem statement/
research questions, objectives, limitations & delimitations and significance of study.
2. The second chapter of the thesis discusses the relevant literature. The literature review
reflects the prior work by the experts of the field to fully clarify the topic. Different
workplace hazards, definition of OHS, principles & practices of OHS, legislation, issues OHS
in developing vs. developed countries, issues of OHS of sugar mills in KP, Pakistan, Health
measures, Safety measures, Welfare measures, Job satisfaction, Employee performance,
theories guiding this research, list of working concepts and Conceptual Framework are
included in this chapter.
3. The third chapter on methodology presents the research philosophies, Approach,
population & sampling, pilot study, sample size, sampling technique, data collection
methods, data analysis tools, list of the working concepts (extracted variables),
operationalization of the concepts, reliability and validity of the instrument.
4. The fourth chapter with the findings and discussions presents descriptive statistics and
hypothesis testing as correlation, regression, mediation analyses and tests of significance.
Before final analysis, data preparation like editing, coding, transformation, detection of
outliers, results of the data normality, results of reliability and validity of the instrument are
discussed. The discussion on the findings and positioning of results within literature are also
presented in the fourth chapter.
5. The fifth chapter is about the conclusion, recommendations, policy implications and future
research directions.
6. The sixth chapter presents listing of citation of all secondary data sources used in this
investigation as references and questionnaire as appendix.
7
Chapter 2: REVIEW OF LITERATURE
2.1 Existing research
Scientific research contributes to a systematic and organized body of knowledge to explain a
phenomenon/ behavior in any area of inquiry following the scientific method, with the help
of discovering laws and postulate theories. Laws are observed patterns of phenomena or
behaviors, while theories are systematic explanations of the underlying phenomenon or
behavior. For example a theory of moving objects is based on Newton Laws of Motion. The
skills needed in conducting research are theoretical and methodological. Soft theoretical skills
cannot be imparted but rather learned though experience. However, standardized
methodological skills can easily be mastered to cater for empirical requirements
(Bhattacharje, 2012). Social sciences including psychology, sociology and anthropology face
the challenge of uncertainty. No variances among natural scientists on the speed of light will
be found, but how to reduce global terrorism will result in numerous disagreements among
social scientists. Unlike natural sciences, social science theories are flexible needing building
newly or improvement through testing (Christos A. Damalas & Spyridon D. Koutroubas,
2018).
Every scientific research starts from the existing literature survey on the topic. The aim of
literature survey was to understand the topic, explore the theory to find out the concepts
relevant to the topic, explore different theoretical models of experts and to extract a custom-
made model for testing in the indigenous setting. Besides different types of issues researched
so far, the methodologies adopted and different techniques of data collection and data
analyses on the topic have been extensively searched out. Current research builds on
secondary data from literature survey, mixes it with the primary data in the form of employee
perceptions, thereby makes full picture.
The literature reviewed is about the different workplace hazards in sugar mills. Occupational
health and safety was searched for definitions, OHS principles & practices, OHS legislation,
OHS issues in developing versus developed countries and issues of sugar mills particularly in
the province of KP, Pakistan. OHS components including HM, SM, and WM were discussed
at length. JS and employee performance (EP) along with their dimensions were thoroughly
8
searched. Different theories guiding this research were also discussed, giving due credit to the
researchers as embedded references in the next section as follows.
2.1.1 Workplace hazards
An occupational hazard means a danger or safety & health risk for workers such as injury
from a fall from height. Hazards are those aspects of the work environment which are
potential source of damage to worker or to all those around. Intensity, duration and individual
susceptibility are the main factors determining the degree of risk for the workers. Preventing
hazards should be based on the SAFE strategy; spot the hazard, assess the risk using different
data collection methods including observation of workplace, interviews of workers and going
through accident records, fix the problem and evaluate the results to find out whether changes
made were effective or not by data collection again. Fixing problem is risk control that aims
to remove a hazard completely (Iheanacho & Ebitu, 2016). Common hazards in sugar mills
are as follows:
a. Physical
Physical environmental factors are harmful with or without contact to bodies of humans like
for example heat may cause prickly heat, heat cramps, heat exhaustion, heat stroke and burns.
Similarly non-auditory effects of loud noise are anxiety and fatigue whereas auditory effects
include acute or chronic noise induced hearing loss. Those regularly exposed to loud noise
must get health surveillance through audiometry along with health education and training.
Sound level meter and the personal dosimeters need to be provided in high risk conditions. If
loud noise exposure is inevitable, then ear plugs and muffs must be provided and used by
employees. Poor light may cause headache, eye-ache, lacrimation, redness, and ocular
irritation, while excessive light may result in glare, photophobia and accidents. Keratitis is
common among welders. Inadequate cubic space makes workers adopt faulty work postures
causing spinal curvatures (P. Katsuro et al., 2010). Radiation hazard (external or internal
radiation after inhalation or absorption) from radioactive material can result in acute or
delayed or hereditary health effects (Van Oldenborgh et al, 2018).
b. Chemical
9
Toxic gases, vapours, fibres and other toxic chemicals may cause damage to workers’ health.
Dust from bagasse in sugar mills may cause bagassosis, which is a serious, progressive
chronic obstructive pulmonary disease in which dust particles <5 micron in size are inhaled
and get settled in the alveoli causing irreversible, nodular fibrosis of lung parenchyma
(Mousa, Fouad, & Bader el-dein, 2014). Chronic cough, dyspnea on exertion, weight loss and
emphysema are common presentations. Besides TB, pulmonary hypertension, corpulmonale,
lung cancer, eye irritation, and dermatitis are the results of chemical exposure. Mandatory
health surveillance in the form of spirometry, chest x-rays, respiratory symptom
questionnaires and health records are important (Dhananjayan, & Ravichandran, 2018).
c. Biological
Biological hazards are also called biohazards. These include bacteria, viruses, rickettsia,
fungi and parasites which may cause dermatophytoses and parasitic infestations (Sawe,
2013). Hepatitis B, Hepatitis C and influenza are very common among sugar mill workers.
Anemia among sugar factory workers is common occupational hematological disorder. Hand
hygiene, gloves, goggles and mask may protect against biological infections (Dhananjayan, &
Ravichandran, 2018).
d. Mechanical/ Ergonomics
Mechanical hazards commonly result in accidents, injuries, and repetitive injuries to body
parts resulting in back & upper limb pain and musculo-skeletal disorders. These are the result
of poor training and health education regarding heavy lifting and handling and poor working
postures due to bad workplace design. Ergonomics (work rules) simply means fitting a job to
the worker (Ahmed, & Shaukat, 2018). Workers using drills and hammers may suffer from
white fingers, bursitis, arthritis, and rheumatic pains due to vibration injuries. Low back pain,
sciatica, disc degeneration, neck pains are common among them.
Risk management includes division of load into smaller units, environment free from
obstacles, level surface, hoists, cranes, vehicles & trolleys, conveyors, adjustable seating,
avoiding over stretching, minimizing bending or stooping, and taking regular breaks. Hand
arm vibration syndrome & carpal tunnel syndrome need risk assessment and monitoring and
10
proper prevention and control in the form of avoidance, substitution, vibration damping,
replacement of worn out tools and rest breaks to limit exposure times and screening (Cai, et
al., 2018). Automation and mechanization with regular inspections of machines, machine
guards, emergency stop buttons, warning signs, worker information, instruction, and training
make a lot of difference (Ahmed et al., 2018).
e. Psycho-social
Psycho-social hazards include stress, low morale, personality & behavior problems and long
working hours. Stress may cause hostility, aggressiveness, anxiety, depression, drug abuse,
smoking, and sickness absenteeism. Poor risk management may result in diabetes mellitus,
cardiovascular problems, infections and cancers. Risk controls include ensuring fairness, time
management skills, workers’ involvement in decision making, no bullying, reporting
unacceptable behavior, clarity of roles and conflict management through training, disciplinary
action, and security measures (Prentice et al., 2018).
2.1.2 Definitions of Occupational Health & Safety (OHS)
According to WHO, OHS is a “complete state of physical, mental, socio-economic, spiritual
wellbeing, and not merely absence of disease and disability among workers and their
families” (Singh et al, 2018). It has been growingly stressed as the fundamental right of all
workers to work in a healthy workplace. The protection of workers against disease and injury
at work is embodied in the preamble of the ILO Constitution. The issues of OHS have
become an integral element of ILO decent work agenda and strong preventive safety cultures.
According to the ILO, every worker has the right not to resume work unless employer has
removed the hazard (Malik et al., 2010). According to Global plan of action on workers’
health 2008-2017 (WHO, 2013) occupational disease prevention and control includes
improving the legal system, implementing the national planning of prevention & control,
enhancing supervision and employers’ responsibility, strengthening prevention & control
agencies, strengthening surveillance & research (Wang & Tao, 2012).
Despite remarkable improvements, there is large incidence and prevalence of occupational
morbidity, disability, and mortality all over the world. Disability has disastrous consequences
for individual, family, society, and employer (Malik, 2010; Jilcha & Kitaw, 2016; Gyensare,
Anku-Tsede, & Kumedzro, 2018). All organizations have a desire to invest in OHS. However
11
Public health, the state, the trade unions, environmental administrators, politicians and the
employers, working in harmony, have a joint responsibility to aspire to the minimum
standards outlined by WHO/ ILO jointly.
2.1.3 Principles & practices of OHS
OHS employs different disciplines like safety engineering, health services, social welfare,
and epidemiological research (Ohuruzor, Adebanjo & Omoniyi, 2014). OHS deals with
ethics, operational issues, rehabilitation, sick leave, fitness for specific work, retirement, risk
assessment & management, health & safety legislation and health surveillance. Health, Safety
and Welfare policies express commitment of organizations regarding health and safety at
work. The five steps of principles of Health and Safety management are; Produce a health &
safety policy, develop a safety culture & attitude, standard setting, OHS performance
measurement and OHS policy review and revision. OHS improves the performance and
reduces unexplained absence, stress leave and turnover (Anwar, Mustafa & Alib, 2019).
OHS concerns with the workforce at individual and group level. It is also concerned with the
customers and the local communities regarding environmental issues. The stakeholders in
OHS implementation are employer, professionals, management, and trade unions. Hence the
need for OHS professionals to remain impartial is very important in successful
implementation. OHS health team consists of occupational physician, nurse, hygienist,
counselor, ergonomist, health, safety manager, physiotherapist, and environment specialists.
To improve practice, OHS audits or detailed examinations should be done. Climatic surveys
regarding trainings & health education practices, workplace inspections employing
checklists, weekly safety tours by management & safety staff, behavior change programs and
benchmarking or intra/ inter organizational comparisons should be undertaken. This would
improve governance along with the involvement of customers’ views. They must observes
the practice against a predefined standard, regarding frequency & cause of sickness/
accidents, reasons of retirement/ death, mission statement, goals, objectives, health survey
data, and compensation data analysis etc. Role of ethical principles, confidentiality and
consent in practice is always very crucial. Occupational health records mostly are now
computerized which need to be secured for the continued success of the OHS service.
12
Appropriate OHS measures by the organizations can reduce injury, illness, disability, death,
and improve overall life quality of workers (Kazmi Saeed et al., 2013).
2.1.4 Legislation of OHS
Health and safety inspectors are responsible to enforce the workplace health and safety law
by inspecting the workplaces, advising the employer, serving improvement notices,
prosecution, accident investigation, report to local authorities, and advice to the public. It is
their statutory right to enter a workplace without notice. They can interview staff and
supervisors, take samples and photographs and seize dangerous equipment. They can pursue
a prosecution in serious cases. Health, safety and welfare regulations protect the safety &
health of young workers less than 18 years of age and expectant mothers. These ensure that
the employer maintains cleanliness, well ventilated building, waste disposal, comfortable
temperatures, task rotation, lighting, minimum space per person, proper seating, maintained
floors & pathways, fences/ covers to protect from falls from height, safe windows & doors,
toilets & washing facilities, drinking water, clothes changing rooms, canteen, and rest areas
etc. Besides provision and use of personal protective equipment (PPEs), control of hazardous
substances, reporting of injuries and diseases, radiation, noise, vibration, and toxic chemicals
etc. are also covered under the regulations. Issues like for example employment,
compensation, human equality & discrimination, work hours, access to information, and
environmental impact assessment also come under the purview of these regulations (Islam,
Razwanul, & Mahmud, 2017).
KP Government enacted factory legislation in 2016 under chapter 3 of the Factory Act 1934
and hazardous occupation rules 1978. The 1 [Khyber Pakhtunkhwa] sugar factories control
act, 1950 (Act no. Xxii). The Act is predominantly socioeconomic in nature and focuses on
workers’ quality of life and OHS. ILO codes of practice provide guidance on OHS in
different sectors. The ILO’s 40 principles on OHS and 40 Codes of Practice are as follows:
1. Promotional framework for OHS convention, 2006 (187) aimed at establishing and
implementing national policies on OHS between government, workers’ and
organizations
13
2. OHS, 1981 (155) aimed at adoption of a national policy and action by governments to
promote working conditions
3. OHS services convention, 1985 (161) for the establishment of enterprise-level
occupational health services with preventive functions
4. Hygiene convention, 1964 (120) for workers in trading establishments for welfare
5. Safety and health in construction convention, 1988 (167) for safety of machines and
equipment used
6. Safety and health in mines convention, 1995 (176) for mine workers
7. Safety and health in agriculture convention, 2001 (184) for preventing accidents and
injury in agricultural and forestry work
8. Radiation protection convention, 1960 (115) to protect against exposure to ionizing
radiations as per technical knowledge available
9. Occupational cancer convention, 1974 (139) policy for preventing occupational
cancer due to exposure over a prolonged period, to physical & chemical agents
10. Working environment (air Pollution, vibration, Noise) convention, 1977 (148) for
hazards due to air pollution, vibration or noise
11. Asbestos convention, 1986 (162) for exposure to asbestos
12. Chemicals convention, 1990 (170) for policy on safe use of chemicals at work
14
2.1.5 Issues of OHS in developing versus developed countries
With ever increasing pace of worldwide technological progress, OHS issues are becoming
more and more global concern, in developing as well as in developed countries. In
developing countries only 10 to 15% of workers have access to OHS. Workers are supposed
to work on their own risk (Bakhsh et al., 2017). Masking of warning shouts and sirens by old
and obsolete machinery noise prevent taking appropriate safety precautions. Workplace
accidents, diseases, disabilities and deaths are costly to workers and their families. Besides
organizations lose precious human resource as direct losses and sick pays, compensations and
poor organizational reputation as the indirect ones (Khaqan, 2017).
In developing world, sugar mills are among the most hazardous job activities having dusty,
dark, hot, slippery and noisy environments. Poor ventilation, exposed electric supply wires
are some of the other issues. Fatigue results from hot ovens and furnaces. Irritability and
hearing loss may result from prolonged exposure to loud noise from big machines. Grease,
acids, alkalis and lime may cause contact dermatitis, eczema and burns. Chronic respiratory
diseases may become manifest after several years/decades of exposure to sugarcane dust
called bagasse. Hazards result in immediate or delayed symptoms depending upon duration
of exposure, individual susceptibility & intensity of exposure. Repetitive strain injuries
causing the musculo-skeletal disorders and cumulative-trauma-disorder as minor back
injuries which end up in disc rupture by lifting too heavy, too large and difficult to reach
loads are common. Majority of workers are not using protective measures due to un-
awareness because of illiteracy (Ohuruzor, Adebanjo & Omoniyi 2014).
In advance countries, on the other hand, the standard of OHS is appropriate as evident from
low incidence of occupational morbidity, disability and mortality as compared to developing
world. Systematic approach of identification & assessment of the risk, data collection &
analysis and implementation of solution followed by evaluation is followed. Performance
management has been practiced in true spirit (Jilcha & Kitaw, 2016). Performance
management is a process of ensuring that set of activities and outputs of an organization, a
department or a worker meets an organization's goals effectively and efficiently. It applies to
aligning employees, building competencies, improving performance for better organizational
results, following three stages of coaching, corrective action, and termination to develop
employees. Performance management success requires Expectation Setting, Monitoring,
15
Development & Improvement, Rating periodically and Rewards as a performance
management checklist. Performance management means creating a conducive work
environment for best performance, eliminating the need for traditional employee performance
appraisals as they don’t work. Performance management begins with job definition and ends
at the exit of employee from organization. It’s an interaction of manager with employees at
every step making every interaction opportunity into a learning occasion.
2.1.6 Issues of OHS of sugar mills in KP, Pakistan
Sugar mill workers were selected as they make a bigger chunk of vulnerable population
exposed to the occupational hazards. Sugar industry, being the 2nd largest after textile in
Pakistan, employs more than 100,000 workers. Pakistan ranks 5th in area and 15th in
production of sugar. It is one of the most labor intensive industries (Ayessaki & Smallwood,
2017). Sugarcane is an important crop for Pakistan as a large amount of sugar is exported and
billions of rupees as foreign currency earned (Khan, Moshammer & Kundi, 2015). Besides,
gur, alcohol, ethanol, bagasse and press mud are the bye-products for paper and chip board
making industries (Munir et al., 2012; Babar & Zaid, 2015; Nawaz et al., 2015). Currently
81 sugar mills are operating in Pakistan. The scope of this study is evident from the above
mentioned facts along with the morbidity, mortality & disability statistics associated with
sugar industry in Pakistan generally and KP specifically. Pakistan is the main sugar producers
in the world. It is an important source of foreign exchange for the country and income for the
farmers.
Sugar mills in KP have hazardous environments as low paid, untrained workers work under
substandard conditions with no inspections and no regulatory controls. Workers often are
treated like machines. Occupational injuries and accidents are common due to poor
awareness of risky situations, lack of fitness, improper carrying and lifting methods, stress,
and bad workplace design (Yusuf, Anis & Novita, 2012). Majority of sugar factories in KP
do not meet the minimum standards and criteria of OHS set by the WHO and the ILO.
Political will is required to update the laws and regulations governing OHS.
One third of the employees in Pakistan are doing overtime duty. Costly legal system with
fragmented piece of law fails to protect workers’ rights at sugar mill workplaces (Gyensare,
Anku-Tsede, & Kumedzro, 2018). This overlooked and overworked community (Rajaprasad,
16
2018) works in close proximity to a range of problems including lack of proper ventilation,
poor lighting, noise, heat, smell, dust, greasy floors, fall from heights (S. Kanchana, et al.,
2015), heavy weight lifting, skin allergy, eyes problem, poor personal hygiene, poor
postures, headache, stress, un-hygienic food and water, unclean toilets, acute and chronic
diseases and many more (Ataro, Geremew, & Urgessa, 2018). The scope of this research will
encompass all sugar mill workers in KP as more or less their work environments and the
related problems are the same.
2.1.7 Health measures
Health measures (HM) include a Comprehensive Healthcar model consisting of Promotive,
Preventive, Curative & Rehabilitative components; an organized approach towards the
management of occupational health & disease related issues through accountabilities, policies
and procedures. HM start with pre-employment medical examination. It includes history
taking, medical examinations and investigations to determine fitness or otherwise of a worker
for a particular job. Besides it serves as a bench mark against which chronic diseases like
bagassosis, TB, asthma, and other could be compared. Periodic medical examination is
carried out at regular six monthly intervals for screening of chronic diseases. Special medical
boards on the request of medical officer to declare a worker unfit on medical ground can be
arranged. Notification of diseases like asthma, pneumoconiosis, cancers, contact dermatitis,
noise-induced hearing loss, and injuries must be done for prevention & control, and
compensation & rehabilitation of workers who become handicapped.
Dispensaries and Social Security Hospital provide regular healthcare services to workers and
their families. The emergency services must be available onsite along with speedy
transportation of casualties. Maintenance & analysis of records, medical surveillance and
research and toxicology are also very important areas in sugar mills (Ohuruzor, Adebanjo &
Omoniyi 2014). Workplace well-being programs include physical environmental initiatives
like lighting, noise, violence, encourage walking/ cycling use of stairs, a smoking policy, staff
counseling, alcohol, time management, healthy eating, and health checks, immunization and
implementation of mental health & substance abuse policies with cooperation of workers.
Personal monitors in the form of film badges, airborne sampling and biological monitoring
through urine or stool samples is necessary. Psychiatric disorders need avoiding stress
17
through individual support interventions as primary prevention and CBT as secondary
prevention (Shaukat, et al., 2018).
2.1.8 Safety measures (SM)
Ninety nine percent accidents are preventable (Ramamoorthy, Thooyamani & Karthick,
2017). All safety related policies & activities with active participation of employees to protect
against Injuries, Accidents, Fires, Electric short-circuits & Explosives are included (Salman
et al., 2016). Safety culture encompasses all safety related values and actions in an
organization. It is a set of attitudes, beliefs, perceptions & habits, developed through the
policies, procedures, and activities with active participation of employers and employees. The
term ‘safety culture’ was presented after the Chernobyl nuclear power plant disaster (1986),
by the International Atomic Energy Agency. Lack of an effective safety culture was behind
this and other global disasters. Commitments at individual and group level regarding
responsibility for safety concerns, active learning, adapt/ and modify behaviors based on
lesson learning from mistakes are relevant. Fire safety, electrical safety and road safety are
the major areas of industrial accidentology (Salman et al., 2016).
Safety climate depends on prioritization of safety training programs, the behavior of the
management to safety, workplace risk level, pace of work, the status of the safety manager,
social status, and the status of the safety committee. Each job must be rated for its potential
for harm or injury. Jobs having high hazard potential should be isolated and training
programs must be considered along with incentives (Kim & Oh, 2015). Safe climate creates
positive attitudes to adopt safe practices among workers. The safety measures boost
employee morale and are likely to express stronger feelings of loyalty to their organization
(Khaqan, 2017). Workplace health promotion programs, combating job stress should consider
work environment’s effect on employees' safe behaviors. Good safety is good business.
Safety measures and performance should not be viewed as competing entities (Veltri et al.,
2007).
All accidents have both direct and indirect costs. Direct costs include liability premiums,
claims for injury, fines awarded by criminal courts, legal costs etc. Indirect costs include
treatment, transport, time lost, loss of production, investigation time and other (Hasnain et al.,
18
2018). It is crucial to clearly understand working conditions and exposures and all other
mechanical hazards including accidents, injuries, fires, electrocution, burns and deaths.
Safety engineers and industrial hygienists, by interacting with clinicians, can better identify
hazards and implement preventive measures. Accidents are a significant cause of dispute
between workers and management. Accidents are preventable. Minor accidents and near
misses if properly registered and investigated, prevent from major ones (Jilcha & kitaw,
2016).
A hierarchy of control is followed which has three components; control at source, prevent/
control transmission of the pollutant to the individual and protect the worker. For example for
noise control at source, consider new tools or isolate machine. Absorbent barriers for
transmission control. Enclose the worker, education, training, supervision, PPEs use,
exposure time reduction, and health surveillance. Safety measures include proper building
design having sufficient cubic space of 500 cubic feet per worker and ventilation for fresh
outdoor air, good house-keeping and proper junk storage to avoid trips and falls, general
cleanliness, wet drilling and mopping of floors, local exhaust ventilation to remove the toxic
gases, mechanical weight lifting, regular maintenance of machines and equipment.
Mechanical mixing of acids and lime, erecting guards around machines, clear access to fire
extinguishers, and immediate cleanups of liquid spills are crucial (Khaqan, 2017).
Job rotation, environmental and statistical monitoring, research and training are other
important safety measures. Health education on safety culture, smoke-free policy in the
workplace, avoid young persons near machinery, cutting off power supply, lifts, stairs,
openings in floors, slippery floors, light, fires, explosives, pressure plants are most relevant
(Shah et al,. 2018). Besides, personal protective equipment (PPEs) should be used as an
additional protective measure. Acids, alkalis, and the lime may cause burns and dermatitis
especially in sugar mill workers. The success of this control depends on the correct choice,
fitting, worn at all times and maintained properly. However, the compliance of PPEs is poor
among factory workers. Liaison and cooperation with the safety committees is important in
certain situations (Ramya, Arepallli, & Lakshmi, 2016). All these measures are direly needed
to comprehend the above said situation in timely manners. Accidents are preventable.
Majority are caused as a result of unsafe acts performed by people. Fatalities are not fated.
These are usually predictable and preventable. Addressing the failures in system by proper
19
management in the organizations keep the workforce in a safe (Ramamoorthy, Thooyamani
& Karthick, 2017).
2.1.9 Welfare measures (WM)
Services that make life worth living thru better QOL come under this heading (Sembe &
Ayuo, 2017). According to ILO, WM refer to various services offered to employees by
employer that make life worth living for the workers and their families as per statute of the
state or local custom. The WM of factory workers are provided in the Factories Act (2007).
According to Oxford dictionary, welfare means socio-economic improvement and respect to
the well-being of the employees to make life comfortable and worth living in addition to the
salary paid (Kadam, Waghole & More, 2012). Welfare activities are intramural and
extramural. Intramural include sitting facilities, retiring room, canteen, lunch place, latrines
and urinals, laundry, earned leave and accident benefits. Whereas extramural activities
include housing, recreational activities, incentives and rewards, family welfare services like
the free education for the children and conveyance for both parents and children. WM
provision, both intra mural as well as extra mural brings win-win situation for both the
employees as well as the employers regarding fulfillment of their expectations (Sembe &
Ayuo, 2017).
Factory laws and Social Security measures bind the employer to develop the minimum
standards for the work environment. Safety and welfare officer must be appointed for more
than 1000 workers. Children less than 14 must not be employed. Adolescents between 15 to
18 years have relaxed working hours. Nine hours/ day with half an hour rest after five hours
of work, 48 hours per week, 60 hours per week including overtime must not be exceeded.
One holiday per week i.e. on Sunday is must. Besides benefits like sickness, disablement,
dependents’ benefits, funeral, dowry, rehabilitation and retirement benefits are in addition to
that (Kamkari, Ghafourian, & Ghadami, 2014). Welfare is critical to the workers’
participation in the success of organization. The WM will increase workers’ performance,
profitability and production as it promotes a sense of belonging among workers, preventing
them from absenteeism and strikes. The relations between employees and employers improve
as a result. WM may be regarded as ‘a wise investment’ which pays back in the form of
greater efficiency. In today’s business scenario characterized by tougher competition,
organizations are more worried about survival at the cost of welfare. The concept of welfare
20
in changed scenario is relevant. Welfare appreciates the value of human resource unlike other
assets which depreciate with every passing year (Manandhar, 2015).
The welfare schemes differ widely with times, regions, country, social values, age, culture,
experience and education of the employees (Kumari & Tatareddy, 2014). Workers are
entitled to risk, overtime and night shift allowances, free/subsidized accommodation and life
and health insurance orders. Besides family welfare programs, free transport facilities,
interest-free vehicle loans, canteen, adequate clean water, free education to children of
workers, training and recreation programs keep workers and their dependents fit and healthy.
The disaster management, stress management, social, educational, vocational rehabilitation
programs, nutrition program, family planning, social services are some of the other areas of
welfare of the workers to keep them motivated. Employees join organizations because of the
wages and salaries along with the facilities and services including housing, transport,
medical, and pension or retirement benefits. Such WM raise morale, improve efficiency of
workers which will in turn affect organization productivity and promote motivation, and
employee’s retention (Waititu, Kihara & Senaji, 2017).
WM include getting workers back to work after disability or illness and remove barriers to
work and rehabilitation through positive attitude of employers towards sickness absenteeism.
These ensure improving access to OHS advice & counseling, good human resource practices,
and retirement schemes. Welfare facilities are provided to make the employees
more efficient. In developed world, the traditional employees’ work appraisal has given place
to advanced concept of performance management (Dar, et al., 2011).
2.1.10 Job satisfaction
How happy & content employees are with their job. A happy worker pays back with high
performance (Fields, 2002). Job satisfaction (JS) is based on positive or negative attitudes,
emotions and feelings towards the job. It is a set of beliefs and perceptions of the employees
about their job that determines their performance in terms of expected quantity and quality.
JS refers to the individual’s overall satisfaction levels from intrinsic factors related to job
content and extrinsic factors associated with the working environment. Satisfied employees
are more committed to their jobs (Fathi, 2015). Job satisfaction is a multidimensional concept
referring to a combination of cognitive, affective and behavioral conditions that make a
21
person to be satisfied with his/her job. Cognitive factors include job benefits, Job value &
related feelings; an individual's perceptions, beliefs, opinions and expectations about duty.
Affective or psychological factors include contentment with the job excitement and
attachment with the job; feelings evoked by feedback that reinforce the individual's self-
worth. Pleasurable involvement represent positive affectivity versus unpleasant involvement
show negative effects (Hoboubi et al., 2017). Behavioral factors include reduced
absenteeism, punctuality at work and low turnover rates (Sembe & Ayuo, 2017).
Job satisfaction is the reaction of an individual to organization or work. Job satisfaction is one
of most extensively discussed issues in organizational management by the psychologists,
managers’ supervisors and employees as a thorough understanding of job satisfaction is a key
to improving the well-being of workers. It means enjoying doing one’s job with enthusiasm
and sense of fulfillment (Jaiswal, et al., 2015). JS is a pleasurable emotional state due to one’s
job experiences; individual's evaluations about different aspects of work. There is growing
evidence that current trends in work environment may adversely affect JS. Job satisfaction is
a concept for a range of attributes and the results of the individual's evaluations concerning
these dimensions versus employee’s aspirations. Job rewards fuel the intrinsic motivation. JS
develops a long term relationship between employee and employer based on mutual trust
(Amponsah-Tawiah K., Ntow, & Mensah, 2015).
JS is determined by the positive perception of the safety, causing low employee turnover
(Fisher, 2003). Job Satisfaction drops significantly in risky and high work load environments.
Job satisfaction, being a motivational phenomenon, determines the turnover intention and
absenteeism of an employee during work (Suresh, Kodikal & Kar, 2015; Shahmin, 2014). Job
satisfaction is considered a critical element for any organization and an important indicator of
workers’ perceptions about the nature of their job (Rageb et al., 2013). JS is a complex
attitude towards one’s task and conditions of the workplace (Sattar & Shadiullah, 2011).
People spend most of their waking hours at work. Satisfied employees are happy and
productive (Unutmaz, 2013). JS increases customer satisfaction and contributes to
competitive advantage of an organization. It is impossible to be satisfied with all aspects of a
job, a reason why it is difficult to assess JS (Qureshi et al., 2013). JS plays a decisive role in
the work behavior of the worker (Leite, Rodrigues & Albuquerque, 2014). JS is a predictor of
employees’ commitment to their organization, to achieve organizational vision and goals
22
(Yucel & Bektas, 2012). Committed employees are highly motivated to work to best of their
ability and resist competitive job offers. Dissatisfied workers, on the other hand, can cause
irreparable damage to a company (Hussain, 2011).
2.1.11 Dimensions of Job satisfaction
Different researchers have taken different determinants of JS as for example a research by
Khan, Moshammer & Kundi (2015), salary, work, supervision, promotion, environment, and
co-workers have been considered. According to Mughai et al. (2016) the salary, job work,
supervision, promotion, coworker and work environment are attributes of JS. This research is
based on same set of JS attributes as follows:
a. Salary
Pay or salary is the main objective of the employees from work. It is a contractual agreement
between the employers and employees who want timely, fair and equitable salaries in relation
to their performance and expect clear policies relating to salaries. Pay increments, bonuses
and benefits affect the job performance (Munisamy, 2013). The performance supported by
financial rewards will be more energetic and motivational (Iqbal, 2013). Compensation
practices heavily influence employee recruitment, turnover and productivity (Hassan, 2016).
b. Supervision
Supervision is the function of guiding the subordinates at work in technical and general
matters, to accomplish designated objectives. Supervising role is difficult and requires good
leadership and communication skills. It needs the ability to treat all employees fairly so that
the employees work energetically (Saeed et al., 2013). The employee participation in
organizational decisions makes them partners in the organizational success and not mere
subordinates. A good working relationship of supervisor with workers is essential in solving
the problems of workers’ strikes and work stoppages (Sattar & Shadiullah, 2011).
c. Promotion
23
Promotion is defined as the advancement in hierarchy to upper level from the lower level of
the company with associated increase in salary, authority, status and the responsibilities. The
fair opportunities to the employees are crucial for the satisfaction and fulfillment of the
higher order needs of workers (Yasir, 2017). Employees must have skills, knowledge and
attitude to perform a job in order to meet expectations. Organization, on the other hand
should give opportunity to employees to use their abilities and skills (Alam, 2012). The
promotion determines the degree of satisfaction of the employees from the policies of an
organization, the commitment, performance and personal growth of the employees. It
increases the reputation of that organization (Hassan, 2016; Yusuf, Anis & Novita, 2012).
d. Co-workers
Humans have a natural desire to interact with others (Alam, 2012). Improving relationships
with colleagues at work would reduce stress. Organizational members working together in
teams will lead to shared performance goals and improved morale of the employees than
working alone hence creating synergy and improved productivity (Nasazi, 2013).
e. Work
Job stress is a universal experience in the life of every employee. Stress is produced when
one cannot properly balance out job demands with personal abilities (Munisamy, 2013). Long
working hours have negative effect on the employee performance as well as the feeling of
alienation from their family resulting in work-family conflict. When their stress is ignored by
the employer the results are increased absenteeism, increased chances of mistakes, high cost,
low productivity, low motivation and the usually legal financial damages. Job satisfaction can
be increased by making job rotation, job enlargement, job enrichment, workload management
and vacation (Alam, 2012).
f. Work environment
Employees obtain benefits from their working environment in terms of deriving a sense of
belonging. Employees get motivation as they feel safe, healthy and comfortable during work.
This ultimately improves productivity of the employees. Unclear organizational policies and
procedures can frustrate employees (Nassazi, 2013). Workers perceiving their work
24
environment conducive are more likely to be satisfied from their jobs. Employees give more
consideration to job tasks and don’t search for better work opportunities (Salman et al.,
2016).
2.1.12 Employee performance
EP is an important multidimensional construct measured through diversity of models (Nawaz,
et al., 2012; WHO, 2017). Employee performance refers to performing the job tasks
according to the prescribed job description and result of what is done or not done (Iqbal,
2013; Nassazi, 2013; Mardani, Tabibi & Riahi, 2012). According to the online dictionary of
Wikipedia, performance is overall expectation of organization from a worker showing a set of
job related behaviors across settings and time (Munisamy, 2013). It is the sum total of the
behaviors carried out by the employees to attain objectives. Performance is a source of
satisfaction, pleasure and pride for the workers as it pays back in terms of financial/ other
benefits such as motivation, competence and discipline (Rageb et al., 2013). Employee
performance is the collective participation as a unit towards the realization of goal of any
organization, which will enable the organization to survive and progress (Anitha, 2014).
EP is workers’ understanding of the organizational objectives and aligning these with the
employees' skills and competencies. Employee performance as an important
multidimensional construct has been studied over decades. Poor performance possibly
challenges and endangers any organization as the success or the failure of an organization is
based on this critical variable (Khan, et al., 2016). Organizations therefore need to empower
their workers through education, training and other means, to attain the competitive
advantage. Institutions and government must ensure that appropriate hazard control
safeguards protect workers and their families through offering incentives to comply workers
with the risk mitigating procedures and policies (Koh, Hegney & Drury, 2011). EP is a
success of fulfilling a job effectively by a person, group or business, by using own potential
to achieve own expectations (Unutmaz, 2013).
EP can be differentiated into contextual and task performance. The skill of a worker to
perform activities according to a specified job description, as for example teaching by a
teacher is the task performance, whereas the contextual performance is the combination of the
individual’s psychological, social and organizational contexts for the task performance.
25
Performance has been a significant key for organizations to gain competitive advantage,
greater productivity, high output and profitability (Shaffril & Uli, 2010).
Every manager wants his/ her subordinates to deliver the best possible performance in order
to integrate their contribution to the overall success of the organization. To discover what
exactly affects work performance among employees is extremely important question to
answer. The answer is professionals’ preparation, their attitude and work behavior, as
motivated by needs, interests and values of all the stakeholders involved. Administration of
industries must dig out the factors leading to employee performance (Amponsah-Tawiah,
Ntow & Mensah, 2015).
2.1.13 Dimensions of employee performance
According to literature review, different models were found. British workplace employee
relations survey, 2004 stated quit rate, absenteeism rate, labor productivity, financial
performance and product quality were the main attributes of employee performance (Jones et
al., 2008). Performance of employees has also been measured in terms of employee/ customer
relationship, productivity, subordinate/ management relationship, turnover intention and the
engagement (Iheanacho & Ebitu, 2016; Gyensare, Anku-Tsede, & Kumedzro, 2018).
Similarly according to another study, quality, cost, accountability, discipline and quantity
were taken as indicators of EP (Mardani, Tabibi, & Riahi, 2012). Efficiency, effectiveness,
economy and quality were studied by Pollit (1988). According to a study published by WHO,
the four indicators of performance were availability, competence, productivity and
responsiveness (WHO, 2017).
Researcher used 4D model of Muharrir & Uphoff, 1994. This widely used model of
definition contains four comprehensive attributes, which are perceived to cover every
dimension of a sample performance including efficiency, effectiveness, responsiveness, and
innovativeness (Sethibe & Steyn, 2016). For successful performance, above criteria must be
fulfilled simultaneously as a package deal and not sequentially. Besides over achievement of
one criterion to the neglect of others will bring suboptimal performance.
These encompass responsibility, targets, quit rate, work ethics, communication,
professionalism and commitment (Irfanullah, 2016).
26
a. Efficiency
Efficiency means achieving the desired objectives/ goals in least time and cost by better
utilization of resources. The ability to undertake an activity using minimum possible
resources such as saving man, money, materials, energy and time. It refers to a match of
inputs in a certain activity and produced outputs from the limited resources. Increased
competition in service provision, among organizations, requires raising efficiency. Efficiency
means performing the processes quickly and to complete work at lower cost (Husebø &
Olsen 2016). Health and safety rules and procedures at workplace help workers to work
efficiently resulting in better performance of employees. Workers understanding the health
and safety tools used for working helps them to work effectively and efficiently (Viva &
Dumondor, 2012).
b. Effectiveness
Effectiveness is the degree of achievement of stated objectives by the employee. Workers
must have a clear picture of set objectives and goals they are to achieve otherwise they won’t
know if they are making progress or not. It is the level of engagement and enablement of
employees to perform at their best. Effectiveness and efficiency are related to the extent that
one complements the other towards the achievement of the organizational goals.
Effectiveness means to adapt to changed circumstances, adopting the right choices. High
level performance is realized through efficient and effective performance of employees
(Qureshi et al., 2013).
c. Responsiveness
Responsiveness is the need analysis of the work environment and inclination and capacity of
workers to respond to outside requests and necessities (Amanullah, 2014).
Responsiveness refers to treating the demands, requirements and expectations of their
customers appropriately. The principle of ‘responsiveness’ is inherent in the concept of
customer service, expectations and perceptions (Husebø & Olsen 2016). Responsive systems
anticipate and adapt to existing and future customer needs, thus contributing to better
outcomes (Mirzoev & Kane, 2017).
27
d. Innovativeness
Innovation means adopting new technologies. Innovation’s importance is very well accepted
worldwide. Organizations need to hire innovative people to drive their innovative goals. One
innovative person, properly placed with other innovative people, through synergy, can
contribute a lot of creative work (Amar & Mullaney, 2017). It introduces novelty in an
organization by improving the organizational outcomes and activities in terms of form,
quality or state. It is a unique and a completely new way of doing things. Companies need the
innovative employees to compete and to drive the goals of the organization. Innovation
requires a vision, team building and communication skills, curiosity and focus, self-
discipline, persistence and desire to help others. Innovation means renewal and regeneration
(Amar & Mullaney, 2017).
Innovativeness means special behaviors. Innovative behaviors enhance productivity.
Creativity is generation of new ideas and innovation is successful implementation of
creativity into new products and services; something that produces economic value.
Innovation is the process of turning ideas into value by identifying a need in others and an
opportunity to meet it or wanting to solve a problem thereby sustaining competitive
advantage (Amanullah, 2014). The challenge is to fully utilize our finite resource towards the
best outcome by managing innovation. For every innovation there is another innovation that
yields a better solution to the problem. Innovativeness inherently involves risks (Sheikh,
Shah, & Akbar, 2018).
Those who do not innovate ultimately fail. Innovation must be proactive and responsive
simultaneously. Customers are becoming more demanding. They want better, cheaper and
more convenient solutions. Competitors are continually striving to meet these demands and
changing trend from physical products towards virtual services. Innovation improves
brainstorming at work, enhances exploitation of new ideas. Innovativeness means ability to
create an atmosphere of accepting diverse ideas, openness and newness of thinking among
workers (Lin, 2006), resulting in the new knowledge and insights development. Health and
safety at workplace has a direct impact on employees' creativity and innovation (Barker 2011;
Altındağ & Kösedağı, 2015).
28
2.1.14 Theories guiding this research
Theories are different views of reality by the experts, made up of variables and their links.
Each link represents one/ more than one established processes or interactions. The job
satisfaction as mediator in the relationship between occupational health, safety and welfare
measures and employee performance can be defined in the theoretical framework as shown in
Figure 2.1 below (Sekaran, 2006). Researcher extracts the underlying theory of the topic and
then uses it as a model for the research in hand. Literature review sets the stage for TFW,
which in turn provides logical base for developing testable hypotheses. TFW is the
representation of the theory behind the research topic. Theory is a building of knowledge,
made up of concepts (bricks) and principles around relationships among concepts (cement).
Questions for generation of new knowledge are the only logical tool in the hands of the
researcher to proceed along the trajectory of the research process. Deductive (theory-testing)
research is more valuable when many competing theories explain the same phenomenon and
researchers aim to know which theory is the best fit in the circumstances of interest
(Bhattacharje, 2012). To explain the effects of occupational Health, Safety and Welfare
programs on employee performance, with the meditational role of JS, many theories behind
the relationships between variables of the study have been explored by the researcher, which
focus on employees and their thinking, attitude and beliefs. Researchers used different
theories and presented different results (Khan, Moshammer & Kundi, 2015).
In deductive (theory-testing) research many competing theories explain the same
phenomenon and researchers aim to know which theory is the best fit in the circumstances of
interest (Bhattacharje, 2012). The ‘happy-productive worker hypothesis’ is the basis of these
theories discussed one by one as follows;
a. The Maslow’s theory
The Maslow’s theory of hierarchical of needs by Abram Maslow (1968) says that
individuals’ needs are arranged in a hierarchy. When one need is fulfilled, another need
emerges to seek satisfaction. This theory has frequently been applied within the context of
industries and organizations, with the assumption that workers believe they can satisfy their
needs through their work (Unutmaz, 2013). Occupational health and safety embraces all the
29
levels of Maslow’s theory: biological needs include food, water, fresh air and clothing etc.
Safety needs are to ensure that employees are non-vulnerable, feeling safe both physically as
well as psychologically like for example security of a home, family and job, insurance,
permanent job, pension and safety from hazards etc. Love and a sense of belonging i.e. social
level is postponed until he feels healthy and safe. Belonging or social needs is the need for
affiliation, for love, affection, to work in teams and meaningful relations with colleagues and
supervisor results in the high JS and performance. Self-esteem or ego needs or needs for
recognition and respect from others and personal sense of competence and need for
achievement enhance performance. He likes to be worthy and distinct from others. Self-
actualization means to develop employee’s full potential and use his abilities to the fullest
extent also enhance their performance (Nawaz et al., 2012).
b. Social Exchange Theory
Social Exchange Theory proposes that the relationships we choose to create and maintain are
the ones that maximize our rewards and minimize our costs. The social exchange theory is
most commonly used by studies in predicting work behavior in field of organizational
behavior. Employers need to treat their employees fairly such that they can reciprocate the
good gesture in the form of behavior. The norm of reciprocity is at the base of Social
exchange theory. The employees respond to a harmful or favorable act in the same coin is the
basis of this theory. Job satisfaction can buffer the relationship between OHS and EP
(Hasanzade, 2013).
c. Herzberg's two-factor theory (1959)
Herzberg's two-factor theory also called motivation-hygiene theory has a practical approach
towards motivating workers. According to this theory, feelings of satisfaction are the
motivators, built-in the job itself, such as achievement, recognition, responsibility and
advancement, whereas the hygiene factors are the interpersonal relationships, salary,
supervision and company policy. Workers satisfied with both groups of factors would be top
performers and those dissatisfied with both would be poor performers. Management must be
concerned with both the groups (Khaqan, 2017). Absence of motivators leads to less
satisfaction instead of dissatisfaction.
30
d. Edwin A. Locke Value theory (1976):
The main premise of the Edwin A. Locke Value theory, value given by a worker to aspect of
work determines how satisfied/ dissatisfied one is when expectations are met or not met
versus those who don’t value that facet. A fully mentally and physically satisfied worker is
the most efficient and effective and content. If an employer takes good care of his workers,
they will improve production. According to this theory, the employer has an obligation or
duty towards his/ her employees’ welfare.
e. The V room expectancy theory (1964)
The V room expectancy theory, states that workers have different goals. They are motivated
accordingly. This theory is based on three variables; Valence, Expectancy and
Instrumentality. Valence means ‘is the outcome I get is of any value to me’. Expectancy
means ‘I believe I can complete the actions’ in terms of probability ranging from 0 to 1.
Instrumentality is the belief that ‘I will get the reward if I perform well’. The product of these
variables creates a motivational force to make worker act in a way that brings pleasure by
linking effort, performance and outcome. The theory holds that individuals choose between
alternatives which involve uncertain outcomes. The individual’s behavior is affected by
preferences amongst outcomes.
Monitory belief attached to particular act is expectancy. The strength of expectations may be
based on past experiences for example the idea that employees who go beyond call of duty
are rewarded. In these circumstances motivation to perform will be increased. Workers can
be motivated and will perform hard if they believe in the worth of their stated goal and if they
think they will achieve it. Management must ensure the required resources to be supplied to
employees. To maintain such employee performance at workplace managers should reward
their employees in accordance with their contribution. This will motivate the employee to
continue performing and even go beyond the call of what they are expected to do.
2.1.15 Concepts searched in literature
According to Sawe (2013), physical environment such as furniture, clean and cold water,
sanitation, proper lighting, ventilation of the building, fire protection, first aid personal
31
protective equipment and health facilities are the main determinants of health and safety
(Yusuf, Anis & Novita, 2012). Viva & Dumondor (2017) has studied health and safety effect
on EP showing that safety, health and welfare facilities and rules and procedures of their job
have a significant effect on employee performance. The four dimensions discussed were the
leadership in safety, safety equipment/ facilities, procedure and supervision (Amponsah-
Tawiah, Ntow & Mensah, 2015; Gyensare, Anku-Tsede & Kumedzro, 2018). The ultimate
aim of all is to improve health status of the workers through comprehensive health care
comprising of the promotive, preventive, curative and rehabilitative care to workforce and
their families and ultimately improve their overall quality of life (Womoh, Owusu, & Addo,
2013).
Job satisfaction results in higher levels of EP due to high morale, discipline, loyalty, and
motivation. Highly fulfilled human resource will perform innovatively, dedicatedly and
creatively (Munisamy, 2013; Shin, et al., 2019). Positive perception with jobs retains
employees and negative perception increases turnover rate. Job satisfaction influences
performance at individual as well as organizational level and is closely related to the quality
of the services provided (Suárez, Asenjo & Sánchez, 2017). Job dissatisfaction leads to
adverse health outcomes, including both physical symptoms and psychological problems
even decreasing lifespan (de Castro, Gee, & Takeuchi, 2008; H. Shahmina, 2014; Inuwa,
2016).
It is well established research guideline that a researcher speaks the language of facts and
figures. Researchers are not supposed to suggest any variable/ s or their inter-relationship/s
on their own. The higher order abstraction is a construct and the lower order abstractions are
concepts. However, in one-dimensional constructs such as weight both are the same. To
measure constructs, variables are used such as IQ score for the construct (intelligence), which
take on differing values for same object, event or person at various times or for different
objects, events or persons, at same time.
Demographic attributes of employees of any organization including sugar mills play
significant role in determining their behaviors towards OHS, JS and EP. A range of research
articles on demographic effects on the study variables were reviewed. Several demographic
attributes have been commonly researched by researchers on these variables, including
gender, age, education, experience, and employment, ethnicity, race, marital status, job title
32
and so on (Qureshi et al., 2013). After a thorough search of literature the researcher has
developed a list of concepts along with their working definitions including five research
variables and four demographic (controlled) variables. OHS and JS have been mostly
researched over the past several decades (Hogstedt, & Pieris, 2000; Siu, Phillips, & Leung,
2004; Lee, & Cummings, 2008; P. Kasturo, 2010; Wang & Yi, 2011; Hussain, 2011; Sattar &
Shadiullah, 2011; Yusuf, Anis & Novita, 2012; Qureshi et al., 2013; Olcer, 2015; Awais,
Malik, & Qaisar, 2015; Munisamy, 2013; Qureshi et al., 2013; Dhananjayan, &
Ravichandran, 2018), due to their close relationship with the employee performance
(Anubhai, 1989; Hussain, 2011; Jankingthong, & Rurkkhum, 2012; Yusuf, Anis & Novita,
2012; Olcer, 2015; Savino & Shafiq, 2018).
2.2 Conceptual framework & Mediation Models
The belief of the researcher about research in the form of conceptual model tells how of the
relationships of relevant variables. The theory explains why of the relationships of relevant
variables i.e. nature/ direction of relationships or hypotheses. Hypotheses support or
otherwise tells whether the formulated theory is valid or not (Theory testing) versus theory
building research. Theories are a set of ideas (concepts) with incomplete number of facts.
Theoretical framework (TFW) is the output of theory about certain topic that consists of
variables, the interconnections among the variables and the processes representing each
connection. Through these TFWs, theories are utilized to implement principles. Theories lead
to knowledge, while every connection between two variables is in accordance with principles.
Literature survey (Chap 2), was conducted to study the experts of this field and develop
theory behind the research issue as TFW or model and devise the field survey action plan.
Variables and their inter-connections have been used as a guideline to conduct a field survey
with a view to testing the theory extracted from the literature. TFW developed serves as a
foundation of research and represents belief of researcher regarding how and why of the
relationships of variables (Sekaran, 2003), between occupational Health, safety and welfare
measures, job satisfaction and employee performance. Woodworm's (1928) independent-
mediator-outcome variable model, states that an active organism arbitrates between stimulus
and response. The theoretical model diagram comprises of the independent variable,
33
dependent variable, the mediator variable and the demographic variables. The schematic
diagrams of the theoretical framework along with mediation models of this study are shown
in the figure 2.1.
Figure
2.1:
Schematic diagram of the Theoretical model
The above mentioned models are the outcome of the critical review of literature. All the
components in the model are justified in the sense that they are explaining the context of our
particular research issue. One can understand how best our research questions are being
addressed as a result & knowledge is properly being advanced.
Researcher’s contribution of this particular study is in terms of both theory building as well
as theory testing. The topic was selected and was followed by review of literature to find out
what are the related variables, their definitions and importance as per the opinion of experts
in the field. As a result a theoretical model was extracted from the theory on the topic. This
implicit TFW representing my topic was tested physically or explicitly in the field. The
model was used as a guideline in data collection and ultimately the model was verified to be
real and explaining the issue. Readings of every connection in the model are the physical or
empirical contributions as blank model is only connected whereas physically tested model is
a contribution to the existing literature by the researcher.
2.3 List of Hypotheses
34
Hypothesis is educated guess, assumption, guideline, tentative solution, supposition, testable
statement or prediction of researcher based on expectation in his empirical data. Testing gives
clues about what to change in situation to solve the problem. Even if the null hypotheses in
not stated, it is implied, because it is converse of the research hypothesis; no difference or
relationship between two or more variables or groups. Theory underlying these relationship/
or differences is the logical explanation of them.
Following are the alternate hypotheses that have been developed to predict the existence of
relationships/ differences mentioned in the theoretical framework to achieve the above
mentioned objectives of Correlation, Multiple regression, Mediation and Testing of
significance of difference.
HA1. EP is statistically significantly & positively correlated with HM, SM, WM & JS
HA2. EP is predicted by HM, SM, WM & JS
HA3. JS strengthens the relationship b/w EP & HM
HA4. JS strengthens the relationship b/w EP & SM
HA5. JS strengthens the relationship b/w EP & WM
HA6. Older employees score higher than Youngers on 5 RVs
HA7. Urban employees score higher than rural
HA8. Educated >10 years score higher than 6-10 years & up to 5 years
HA9. Experienced workers>5 years score higher than up to 5
35
Chapter 3: MATERIALS AND METHODS
Methodology refers to the research strategy or plan about conduction of research. Research
design guides data collection and analysis in relevance to the research problem. This chapter
presents the methodological procedure adopted to achieve the answers to the pertinent
research questions to fill the research gap. The research philosophy, Approach, population
and sampling, pilot study statistics, sample size, sampling techniques, data collection
methods, data analysis plan, list of working concepts (extracted variables), operationalization
of concepts, reliability, validity, and ethical considerations have been described in this
chapter.
3.1 Research philosophy
Two popular paradigms of Positivism vs. Interpretivism, for human inquiry are characterized
in terms of ways in which they respond to basic philosophical questions, ultimately
determining the action (methodology) of researcher (Remenyi et al., 1998; Gummesson,
1991).
1. Ontology: Objective reality existing independent of humans with single interpretation
as for example the topic ‘Effect of occupational health on employee performance with
mediating role of job satisfaction’ OR subjective reality is creation of mind of
participants with multiple interpretations
2. Epistemology: Researcher is independent OR interacts with participants
3. Axiology: Person beliefs or biases kept in check OR inevitable
We selected Positivism which maintains whatever the source of knowledge, it must be
verified empirically (Scientific method) through hypothetico-deductive processes, knowledge
in the form of interrelated concepts, quantitative analysis & generalizations OR qualitative
research with flexible design and qualitative analysis as for example research topic on suicide
bombing.
36
3.2 Approach
Survey approach wherein a representative sample from the total population is selected onto
which the findings of the sample were generalized (Kim & Oh, 2015). Survey approach was
chosen on the logic of frequently used in large population, Quick, easy, inexpensive to solve
the problem by answering 5Ws and 1H of any research question. Survey approach is
considered reliable and accurate and permits internal validity (Babbie, 1995).
Research design means rules governing the research process. According to criteria laid down
by Sekaran (2003), this was a comparative cross-sectional, correlational survey, since data
was obtained using only one-time survey, allowing the nature to take its course with no
manipulation of variables/ conditions. The intent of the study was to describe the
characteristic as well as study relationships/ differences among groups (hypotheses testing)
which were possible through comparative cross-sectional survey. It is the most frequently
used method in social sciences to measure the respondents’ perceptions (in terms of primary
data) in a large population regarding any problem, in short time period and that too in an
economical way (Cameron, 1981; Cameron & Freeman, 1991; Babbie, 1995; Kim & Oh,
2015; Malik et al., 2010).
Unit of analysis was organization. However, perceptions were recorded of the employees of
six functional sugar mills of KP, Pakistan, from December, 2016 to March, 2017. Data
collection and processing took four months. Research from many countries shows benefit of
data collection from industrial workers to study the EP and its determinants (Gyensare, Anku-
Tsede, & Kumedzro, 2018).
3.3 The Population and sampling design
Target Population or reference population of any study is the entire group of characteristics
(elements) that researcher wishes to investigate and make inferences to, based on sample
statistics. The population comprised of all the employees of all six functional sugar mills in
KP, Pakistan having 3956 employees as per the most recent data as of November, 2016 on
the web sites of respective mills.
3.4 The pilot study (n=36)
37
The instrument was discussed with a panel of experts from the Departments of Public
Administration, Gomal University and Qurtaba University, D.I.Khan and Department of
Community Medicine, Gomal Medical College, D.I.K, Pakistan to improve the alignment of
wordings of the instrument with the objectives of the study. Based upon feedback received
some double-barreled, leading and loaded items were modified or rephrased. This improved
the level of understanding and communicability of the questionnaire (Hair et al., 2010). A
pilot study was then conducted with the objectives of determining the sample size for the
study, assessing the participants’ understanding of the items, calculating the data dispersion
and estimating the reliability and validity of the questionnaire. Participants in a pilot study
were selected from sample of the prospective sample (Booth-Kewley et al., 1997). The
researcher used 40 respondents selected on convenient basis from sugar mills. However, 36
completely returned questionnaires were analyzable. We analyzed them to find out the
sample size and reliability of the questionnaire. However pilot study data was not included in
the main study. Pilot study enabled us to develop synopsis which after approval became the
base of the main study.
3.5 Sample Size
The sample size was 319 estimated on the statistics of the pilot study. The procedure of
determining sample size is as follows in table 3.1.
Table 3.1 Computation of the SS for population of sugar mill employees of KP, Pakistan
z-value at 95%
Confidence SD Margin of Error (e) Population (N)
Sample
Size (n)
1.96 0.58 0.061 3956 319
Z α/2= the standard normal coefficient (A confidence level of 95% with an (α/2) of 0.025
resulted in a coefficient of 1.96), SD = the standard deviation (from pilot study), e = the
desired precision/ acceptable margin of error of +/- 2% (0.02 x 7 point Likert type scale), N =
known population size and n= sample size.
38
The desired precision/ acceptable margin of error is a subjective decision. For instance a
maximum of 3% is accepted for continuous data (Bartlett, Kotrlik, & Higgins, 2001). We
used .o61as margin of error for reliability of results. Sample size was calculated using a finite
population correction factor that yields 319 as corrected sample size n.
As continuous data (36 items measured on 7 point Likert scale), hence formula for
continuous variables by Bartlett, Kotrlik, & Higgins (2001) is suitable. Hines and
Montgomery (1990) practiced ‘z’ test (1.96) with level of significance is to be (0.05), the
statistics of dependent variable (research/ test variable) is used in the formula for determining
sample size for the main study. To increase precision, confidence or both (to decrease SE)
given a particular SD in a sample, we need to increase sample size unless sigma is low.
3.6 Sampling Technique
Representative sample is possible to generalize provided normally distributed attributes/
characteristics of population follow the same pattern in sample. To ensure that everyone in
the population has an equal chance of being selected in the sample, Proportionate Stratified
random sampling, one of the most efficient probability sampling design according to Sekaran
(2003) which was appropriate for the present study was used. Sekaran (2003, p. 273)
proposed that proportionate sampling decisions are made when the strata are neither too small
nor too large, as in present study (see Table 3.2). Therefore disproportionate stratified random
sampling was decided. Mill workers in northern & southern regions constituted two strata.
Northern region comprised of population of two working mills; Khazana sugar mill,
Peshawar and Premier sugar mill, Mardan having 1266 employees. Southern region had four
working sugar mills; Chashma-1 sugar mill, Chashma-2 sugar mill, Al-Moiz sugar mill,
Miran sugar mill having 2690 employees. Two mills were selected, one mill each from each
strata on the basis of simple random sampling technique. Permission from management of
Khazana Sugar Mill, Peshawar & Chashma Sugar Mill-1, D. I. Khan was sought. Sampling
frame for both the mills was formed, out of which the sample was selected using simple
random sampling technique. Sample comprised of 103 subjects from northern and 216 from
southern region (Table 3.2). All employees were eligible. Refusal to respond to the
questionnaire was the only exclusion criteria. Out of total 319 distributed questionnaires, 263
were received as usable for analysis. Our return rate was 82% which is acceptable.
39
Table 3.2 Proportionate Stratified Random Sampling
S No REGIONS N SD N
1 Northern 1266 0.073 103
2 Southern 2690 0.063 216
Total
3956
319
3.7 Data Collection Methods
Data collection must be in line with the problem statement, objectives and the study
hypotheses. Data collection was done from December 2016 to March 2017, spanning over a
period of 16 weeks. Working two days per week make about 32 days of data collection. Data
was collected by a trained data collector. The trained data collector was paid honorarium for
collecting data. For secondary data literature survey was conducted, whereas the
questionnaire was used to collect primary data as per needs of our research project and
feasibility to collect opinions of the workers about a problem.
3.7.1 Literature survey
Literature survey refers to documented research on the topic available in publications, official
reports, websites, databases & books which was conducted by subject, by author and by title.
Key words and phrases were used for theses, on line articles and websites. Publications were
searched with reference to the topic in national and international literature including
ScienceDirrect, Google Scholar, Pub-Med, HEC Pakistan Research Repository and Cochrane
database. Keywords such as Performance Management; Perform; Sugar Mill; Pakistan;
Developing Countries; Developing World; Developed Countries; South Asian countries,
Occupational Health & Safety; Occupational Health; Hazards were given. Besides OHS,
OSH, Factory workers were some of our key words.
3.7.2 Field survey
Field Survey was conducted thru a structured questionnaire (extracted from literature),
containing all variables (36 items on 1-7 Likert scale). Primary or first-hand data is collected
40
by the researcher using Questionnaire, Interview and Observation, whereas secondary or
qualitative data comes from existing sources i.e. up-to date data collected by someone other
than researcher to be used in current study, to save time and cost. The questionnaires were
collected directly after the workers filled them in the mills by the data collector. It was
verbally translated into the native languages of workers and questionnaires were collected on
spot.
The measurement tool in research for testing the research hypotheses should be valid and
reliable, for highest quality and lowest number of errors. Thus, the instrument must
accurately measure what it is supposed to (Cooper & Schindler, 2001). Sekaran (2003) says,
researchers must use reliable tools that have already been tested, instead of their own
measures. Accordingly, the questionnaire of study is a combination of several standardized
instruments, regarded as reliable and valid and which were further adapted to local context
f/b verification by literature review (Positivistic philosophy flowing through each stepn of our
research).
The questionnaire has five research variables. HM (5 items) & SM (5 items), using
Questionnaire by ‘Work Environment Survey by Newfoundland and Labrador Statistics
Agency (NLSA) 2008. WM having 5-item measure by ‘Employee welfare measures in
DGVCL’. JS with 12-items used ‘Survey by Spector (1997)’. EP contains 9-items used
questionnaire by Babin and Boles (1998).
The questionnaire was having 7-point Likert scale which converts the qualitative variables/
data into quantitative (numeric) variables/ data for better descriptive and inferential analysis
and interpretation. The questionnaire had two sections; the first section contained
demographic profile while the second section had items for measuring the five research
variables from sugar mill workers. The primary data was collected through questionnaire
consisting of 36 items; 5 for HM, 5 for SM, 5 for WM, 12 for JS, 9 for EP and 4 for
demographic variables. The questionnaire was in Urdu.
3.8 Data Analysis Tools
Data analysis is the process of answering research questions set forth in the beginning of the
study. The researcher used 2 methods: 1.Thematic analysis’ for qualitative or secondary data
41
& 2. Statistical procedures for quantitative or primary data with SPSS, running correlation,
regression, TOS, reliability & validity analyses (Zickmund, 1997; Saunders, 2003:89;
Mohasi, 2014).
3.8.1 Qualitative Data Analysis (Theoretical Network Approach)
Theoretical Network Approach was used for Qualitative data analysis. This approach
employs thematic analysis based on ‘Argumentation’ and ‘Grounded-theory’. First of all
author-wise Cards related to the topic are prepared, which are then categorized variables-
wise. Finally logical and chronological sequencing is done as guided by the Argumentation
theory. The entire procedure is shown in Figure 3.1.
Figure 3.1 Theoretical Network Approach to Qualitative Data Analysis
3.8.2 Quantitative Data Analysis
a. Descriptive analysis
42
Univariate analysis through frequency distributions and percentages along with measures of
central tendency and measures of dispersion were calculated to describe the IVs and DV. The
four demographic variables (Age, Residence, Education and Experience) were presented as
frequency distributions. To test the assumption of normal distribution of the population,
numeric data (HM, SM, WM, JS and EP) were subjected to histograms and skewness/
kurtosis statistics descriptively, confirming the normality of distribution of all research
variables. Mean, minimum, maximum, range and SD were calculated as data was normally
distributed. Overall score for the five research variables was presented in a single table.
b. Inferential analysis (testing of hypotheses)
i. Pearson test of correlation
Pearson test of correlation as bivariate analysis tool was applied to see the correlation
(strength and direction) between EP on one hand and four other research variables on the
other respectively. Alpha of 0.05 was considered as statistically significant.
ii. Regression analysis
Multiple regression as multivariate analysis tool aims to predict a variable of interest from
several other variables was used; a powerful statistical technique of parametric data. Step-
wise Multiple regression was applied to check the cause-effect relationship between the
predictors (HM, SM, WM & JS) and criterion EP to inspect the degree of variance in
outcome variable because of predictors (Hair, et al., 2010).
iii. Tests of mediation
Methods to explain the causal mechanism or process of a known relationship between
predictor and criterion is called mediation analysis, whereas moderator affects the strength of
this relationship. This study followed Baron and Kenny (1986) mediation model to test the
role of mediator variable in the cause and effect relationship between IVs and DV.
Preconditions for mediation include; when predictor significantly influences the mediator, the
mediator significantly affects the outcome, the IV significantly influences the DV in the
absence of the mediator and lastly the IV effect on the DV shrinks by adding the mediator in
43
the regression-model. In other words, all the pathways i.e. a, b, c and c hat must be
significant. Mediator can either strengthen or bolden the line showing the x-y relationship
(partial mediation) or it can disconnect it totally (full mediation). Practically we tested path
‘a’ by simple regression, then path ‘b’ followed by hierarchical regression in which path ‘c’
was first tested, and then ‘c prime’ was tested. A specialized t test to determine whether the
decrease in the effect of the predictor, after mediator inclusion in the model, is significant
(mediation effect is statistically significant or not) is the Sobel test. Complete mediation,
means the total effect of a predictor on a criterion is conveyed through one or
more mediator variables indirectly with no direct effect indirect.
Figure 3.2 Baron & Kenny (1986) Mediation-Model
iv. Tests of significance
This method allows researchers to explore bivariate analysis through tests of difference to
answer their respective questions; whether or not continuous variables (outcome variables)
and a categorical variable (demographics) are related. The independent samples t-test was
applied for 2 attributes each of Age, Residence and Experience, whereas one-way ANOVA
test was applied for three attributes of Education to see the significance of difference. Means,
SD, test value, degree of freedom and level of significance as p-value were mentioned. 0.05
was taken as statistically significant alpha value.
3.9 List of the Working Concepts (extracted variables)
44
All the demographic as well as research variables of interest along with their working
definitions are given in table 3.3 below:
Table 3.3 List of the extracted research variables along with definitions
S. No. Variables Definitions Codes
1 Health
measures
All those measures taken for workers regarding
prevention and control of occupational diseases and
illnesses against health hazards.
HM
2 Safety
measures
All those measures taken for the workers regarding
protection from occupational injuries and accidents
against safety hazards.
SM
3 Welfare
measures
All those measures taken for the workers regarding
improvement of living standard of the workers and their
families.
WM
4 Job
satisfaction
The level of contentment of the worker from his/ her job
at the workplaces. JS
5 Employee
performance
The actual outputs of workers versus their intended
outputs at the workplaces. EP
Table 3.4 List of the extracted demographic variables along with definitions
S. No. Variables Definitions Codes
1 Age Age groups of the respondents AGE
2 Residence Residence of the respondents RES
3 Education Education of the respondents EDU
4 Experience Experience of the respondents EXP
3.10 Operationalization of the Concepts
45
The synonymic definitions given in dictionary of a construct are not particularly useful in
scientific research, which needs operational definitions of constructs for empirically
measuring and elaborating the meaning and content of that particular construct. Objective
variables such as demographic variables are easy to define whereas subjective (abstract)
variables/concept or construct like for example ‘perceptions’ are difficult to measure. They
need definition followed by reduction to observable behaviors/ characteristics (domains or
dimensions), which are then broken into items for valid and reliable measurement.
Table 3.5 Operationalization of research variables
Variables Attributes Items Source
Health
measures
Healthcare services, Health education,
Display of instructions, Knowledge of
OHS regulations, Record-maintenance
1-5 (Hogstedt, & Pieris,
2000; Ali, & Davies,
2003; Siu, Phillips, &
Leung, 2004; Qureshi et
al., 2013).
Safety
measures
Inspections, Safety equipment, Regular
audits, Job-specific trainings & refreshers,
Accident investigation
6-10 (Hogstedt, & Pieris,
2000; Ali, & Davies,
2003; Siu, Phillips, &
Leung, 2004; Qureshi et
al., 2013).
Welfare
measures
Residential facilities, Transport,
Education, benefits 11-15 (Hogstedt, & Pieris,
2000; Ali, & Davies,
2003; Siu, Phillips, &
Leung, 2004; Qureshi et
al., 2013).
46
Job
satisfaction
Pay; Financial return for the work done,
according to the experience
Promotion; Frequent, fixed, up-gradation
to the next higher rank based on fair
performance evaluation
Supervision; Care and praise given by the
senior staff/ Fair delegation of work
assignments, understand problems and
give say in decisions
Colleagues; Care given by the workers at
similar position/ atmosphere of trust and
respect, sympathetic, provide guidance
and assistance
Work itself; What is done by a worker
matching with his knowledge and skills,
respectable. Gives sense of achievement
Work environment; Comfortable with
policies/ understand goals and objectives
of the company
16-17
18-19
20-21
22-23
24-25
26-27
(Ali, & Davies, 2003;
Lee, & Cummings,
2008; P. Kasturo, 2010;
Wang & Yi, 2011;
Hussain, 2011; Yusuf,
Anis & Novita, 2012;
Qureshi et al., 2013;
Olcer, 2015; Awais,
Malik, & Qaisar, 2015;
Munisamy, 2013).
Employee
performance
Efficiency; Quantity of product is much.
Resources are saved.
Effectiveness; The quality of product is
good. Quality of work is good.
Responsiveness; Employer satisfaction is
important for me. Supervisor
requirements is important for me.
Innovation; New technological methods
in work are welcomed. I fully accept new
ideas by the management. Workers
constantly improve their services as per
the changing requirements of the market
28-29
30-31
32-33
34-36
(Anubhai, 1989; Ali, &
Davies, 2003; Hussain,
2011; Jankingthong, &
Rurkkhum, 2012;
Yusuf, Anis & Novita,
2012; Olcer, 2015;
Savino & Shafiq, 2018).
47
Table 3.6 List of the demographic variables
S. No Variables Attributes Sources
1 Age 19-40 years, 41-60 years (Kubeck, et al., 1996; Qureshi et
al., 2013).
2 Residence Urban and rural (Kubeck, et al., 1996; Qureshi et
al., 2013).
3 Education Up to 5 years, 6-10 years, > 10
years
(Kubeck, et al., 1996; Qureshi et
al., 2013).
4 Experience Up to 5 years, > 5 years. (Kubeck, et al., 1996; Qureshi et
al., 2013).
Mixed tools and methodologies are neither good nor bad. It is the requirement of the
researcher and situation that determine their use or not to accomplish his/ her objectives. We
started with qualitative methodology by selecting the topic, consulted the existing research,
came up with a TFW and data collection on likert scale. We switched to quantitative
methodology in data analysis by coding of the questionnaire to make measurements from
statements. Again Qualitative argumention was adopted in discussions, conclusion and
recommendations.
Research variables of this study were; HM, SM and WM (independent variables), JS
(mediator) and EP (dependent variable). Demographic variables were Age; 19-40 years, 41-
60 years, Residence; urban and rural, Education; up to 5 years, 6-10 years, > 10 years, and
Experience; up to 5 years, > 5 years. Scales of measurement (data types) of the variables
were; Residence was tapped on nominal, whereas Age, Education and Experience were
measured on ordinal scales. OS, OH, JS and EP were all interval data.
3.11 Reliability
Reliability of a scale indicates the extent it is free from random error or bias. Reliability is the
degree of dependability of the measure of a construct. For example the guessing of weight
measurement is unreliable versus using a weight scale. Reliability implies consistency but not
accuracy. If the weight scale is calibrated incorrectly, it will not measure correctly. Therefore
48
not a valid measure, but will still give reliable readings. One of the important sources is the
observer’s bias. Employee morale defined by smiles on a very busy day or a light day is read
differently an observer or even by two observers on the same day, depending on their smile
perception. Second source is subject bias for example asking people about salary as may be
perceived as monthly, annual, hourly etc. A third source is technical bias. Besides avoiding
above mentioned problems, there are multiple ways of estimating reliability.
Cronbach’s alpha is a useful technique to assess the reliability. Internal consistency is the
degree to which the items are all measuring the same underlying attribute or hanging together
as a set or average correlation among all of the items is indicated by Cronbach’s alpha (Hair
et al., 2010, p.125). This implies that the respondents actually understand the questions as a
single concept. Cronbach’s coefficient alpha < 0.70 is considered weak and > 0.80 as good
reliability (Nunnaly, 1978; Hair et al., 2010, p. 125; Sekaran, 2003). As it is observed that all
the Cronbach’s alpha values for each construct were above the cutoff value i.e., 0.50, which
indicated reliability of the instrument, results given in chapter four.
Test-retest reliability means stability across time and items or low vulnerability to changes in
situation. Same people are administered at two different occasions, with a gap of 1-6 months
and calculating the correlation between the two scores or same group administered two
similar questionnaires with different wordings and sequence; Correlation coefficient higher
the better. Inter-rater reliability or inter-observer reliability, of the same construct is usually
assessed in a pilot study. In interval or ratio scale, simple correlation between measures from
the two raters can estimate the reliability. Split-half reliability is between two halves of a
construct measure. For example, ten-item measure randomly split into two sets of five and
administer the entire instrument to a sample of respondents. The correlation between the total
scores in each half is split-half reliability. Internal consistency of the responses as checked by
the Cronbach’s alpha test on the the instrument having continuous scale are as follows.
Tables 4.12 to 4.16a present the summary of calculated coefficient alphas for the 5 items of
HM, 5 of SM, 5 of WM, 12 of JS and 9 of EP showing that all coefficient alpha values for the
total items and for each scale ranged from 0.550 to 0.915 and are in the acceptable range,
which proves sound reliability of the instrument.
3.12 Validity
49
The research quality depends on goodness of the data use, which in return depends on the
goodness of the instrument. The goodness of the instrument means reliability and validity.
Validity is studied after reliability but comes before it at the time of construction of
instrument. It is the ability of the instrument to measure exactly what it was made for. The
questionnaire of the study is a combination of several standardized instruments. The
dissimilar context necessitates some minor modifications to validate the instrument in local
population. Factor analysis for construct validity and Cronbach’s alpha for the internal
reliability of the instrument were performed. Statistical software known as Statistical Package
for Social Sciences (SPSS) version 20 for windows was used to perform all these analyses.
Construct validity evaluates the degree to which a measure correctly measures what it is
purported to measure (Baroudi & Orlikowski, 1988; Hair et al., 2010). For example a concept
named compassion really measuring compassion and not empathy. Construct validity was
statistically found by factor analysis, for making sure that the set of items represents a
construct on the pretested scale in pilot study. Pilot study data with only 36 filled
questionnaires, was insufficient and therefore indecisive, versus 100 suggested by Hatcher
(1994). Hence, on total sample size the Factor analysis was repeated. Prior to performing the
factor analysis, following assumptions should be checked:
1. Outliers not accepted
2. Linearity (No Multi-collinearity): VIF < 10 (Hair et al., 2010)
3. Should be normally distributed data (Hair et al., 2010)
4. Sample Size Minimum: 5 Cases to each study item (Tabachnick & Fidell, 2007)
5. Significant Bartlett’s Test of Sphericity (p < .05) (Tabachnick & Fidell, 2007)
6. Kaiser-Meyer-Olkin (KMO) Index ≥ 0.5 (Hair et al., 2010)
The Kaiser-Myer-Olkin (KMO) varies from 0 to 1.0 and should be 0.60 or higher to proceed
with factor analysis (Tabachnick & Fidell, 2007) as it is a measure of sampling adequacy
(Hutcheson & Sofroniou, 1999; Field, 2005).
Factor analysis was employed to test the construct validity to ensure that the set of items
represents a sole construct (i.e., convergent validity). All scales were subjected to factor
analysis of responses to the questionnaires (n =263) using principal component solution with
a varimax rotation method to improve the interpretability of factors through rotation. As
50
shown in tables 4.7-4.11 and figure 4.7-4.10, the minimum recommended factor loading for
EFA is 0.40. The factor loadings of all the scales were noted almost excellent i.e., above 0.71
and almost all the scales items loaded exactly on their respective factor constructs with the
exception of only 4 items, 1 each of HM, SM, WM and JS, that were loaded differently
stating sound validity of instrument of present study. KMO values for all variables were
recorded. BTS values and Factor loading were recorded for all the items by using a principal
component solution with a varimax rotation, one factors each having eigen value greater than
1 was explored conforming to respective constructs for HM, SM & WM. For JS, five factors
having eigen value greater than 1 and 0.7 were explored conforming to respective constructs
including pay, promotion, co-workers, supervisor, colleagues, work and work environment
loaded onto their respective factors with all showing very good loadings. Varimax rotation
with fixed number of factors at 4 validates 4 factors i.e. efficiency, effectiveness,
responsiveness and innovativeness. having eigenvalue greater than 1 were explored
conforming to respective constructs for EP. Tables 4.7 to 4.11a and figure 4.6 to 4.10 present
the summary of validity analyses.
3.13 Ethical Considerations
The respondents were approached after taking permission from the concerned authorities of
the mills. The purpose of the study was explained to the respondents and their consent was
sought. Strict confidentiality was maintained in collection of data, analysis of data and
presentation of findings to maintain the confidence of respondents and safety of all the
respondents.
Chapter 4: RESULTS AND DISCUSSION
In this chapter the field survey results are presented as hypotheses testing, extracted from the
TFW to answer the research questions. The raw data collected from the questionnaire was
51
properly prepared before applying different advanced statistical techniques for reliable results
as for example editing and missing responses, coding, categorization and data transformation,
outliers, adequacy of fit, and goodness of data (validity and reliability) (Sekaran, 2003; Beri,
2008).
4.1 Data Preparation for Analysis
4.1.1 Editing and missing responses
A total of 263 out of 450 questionnaires were identified valid for final analysis, whereas 22
questionnaires were omitted due to either wrong or incomplete filling, thereby not
compromising the final results.
4.1.2 Data coding
Mutually exclusive (Emory, 1998) and collectively exhaustive coding for all responses were
absolutely considered. Being closed ended all answer options of all the items of questionnaire
were therefore already coded, for putting into the data matrix of SPSS. The answer options of
the question on Age were coded as 19-40 years [1] while 41-60 years [2]. Residence was
coded as [1] for urban and [2] for rural, Experience as [1] for up to 5 years and [2] for >5
years and Education as [1] for up to 5 years, [2] for 5-10 years and [3] for >10 years. The
second part of the questionnaire measuring research variables were measured on 7- point
Likert scale. These were coded as: Strongly disagree [1], Moderately Disagree [2], Disagree
[3], Neutral [4], Agree [5], Moderately Agree [6] and strongly agree [7].
4.1.3 Categorization and data transformation
There was no negatively worded question needing reversal before entering data into SPSS
(see questionnaire appendix). Additionally, attributes (items) were transformed into a single
research variable by taking their average for subsequent analyses.
4.1.4 Outliers
An outlier is a much larger or lower value than the rest which may distort the average value
(Kleinbaum, Kupper, & Muller, 1988) and inferential outcomes (Tabachnick & Fidell, 2007).
The frequent cause of outlier problem is miscoding and respondents’ errors. It can be
52
discarded if not dealt with accordingly and as suggested by Field (2005) and these were
deleted.
4.1.5 Adequacy of fit
Statistical techniques need certain assumptions for the adequacy of fit between the data and
the statistical analysis technique respectively. Four assumptions of parametric tests are
normally distributed data, Interval data, Homogeneity of variance and independence. The
assumptions related to statistical tests are considered in the later part of this chapter. Here,
normality of variables is discussed which is one of the main assumptions for parametric
statistics. Before proceeding for data analysis, data should be normal because parametric tests
used on non- parametric data give inaccurate results.
Skewness refers to asymmetry of a distribution. Positive skewness shows too many low
scores piling-up on the left of the distribution and vice versa. Positive kurtosis shows a pointy
distribution versus flat distribution in negative values. The closer the value is to zero, the
more likely the data are normally distributed. According to Hair et al., 2010, the numeric data
is considered to be distributed normally if the skewness and Kurtosis statistics are (–1 to +1).
According to Field, 2005 if sample size is 100 then skewness and kurtosis value should be
between + 1.96, if sample size is 200 then skewness and kurtosis value should be between +
2.58 and if sample size is more than 300 then it should be between + 3.29.
For the present research, due to lack of knowledge regarding the population distribution, the
normality of variables was examined by graphical as well as quantitative methods. The
histograms of all the five research variables appear to be distributed approximately normally,
centred on respective mean values, although a few are somewhat skewed. However moderate
departures from normality with sample sizes larger than 50 is little cause for concern. The
results of data normality are given as follows:
Table 4.1 Skewness & kurtosis statistics of sugar mill employees data of KP, Pakistan
(n=263)
Skewness Kurtosis
Statistic Std. Error Statistic Std. Error
HM -.866 .150 1.496 .299
53
SM -1.129 .150 1.576 .299
WM -.864 .150 1.530 .299
JS -.313 .150 -.628 .299
EP -.160 .150 .061 .299
Table 4.2 Statistics of the distribution of HM of employees in sugar mills of KP,
Pakistan(n=263)
HM HM HM HM HM
Mean 5.84 5.81 6.11 6.10 4.36
Std. Error of Mean .083 .078 .064 .062 .040
Std. Deviation 1.344 1.264 1.039 1.001 .650
Skewness -1.426 -1.260 -1.611 -1.480 -.271
Std. Error of Skewness .150 .150 .150 .150 .150
Kurtosis 1.231 .972 2.719 2.522 -.457
Std. Error of Kurtosis .299 .299 .299 .299 .299
Table 4.3 Statistics of the distribution of the SM of employees in sugar mills of KP,
Pakistan (n=263)
SM SM SM SM SM
Mean 5.84 5.81 5.89 6.11 6.14
Std. Error of Mean .083 .078 .076 .063 .061
Std. Deviation 1.344 1.264 1.234 1.015 .993
Skewness -1.426 -1.260 -1.429 -1.614 -1.659
Std. Error of Skewness .150 .150 .150 .150 .150
Kurtosis 1.231 .972 1.561 2.897 3.207
Std. Error of Kurtosis .299 .299 .299 .299 .299
Table 4.4 Statistics of the distribution of the WM of employees in sugar mills of KP,
Pakistan (n=263)
WM WM WM WM WM
Mean 6.11 6.10 6.24 5.90 5.84
Std. Error of Mean .064 .062 .054 .063 .066
Std. Deviation 1.039 1.001 .870 1.022 1.072
Skewness -1.611 -1.480 -1.579 -.644 -.688
54
Std. Error of Skewness .150 .150 .150 .150 .150
Kurtosis 2.719 2.522 3.080 -.592 -.410
Std. Error of Kurtosis .299 .299 .299 .299 .299
Table 4.5 Statistics of the distribution of JS of employees in sugar mills of KP, Pakistan
JS1 JS2 JS3 JS4 JS5 JS6 JS7 JS8 JS9 JS10 JS11 JS12
Mean 4.19 3.39 4.21 4.27 3.14 4.30 4.35 4.40 4.37 4.33 4.38 4.36
Std. Error of
Mean
.067 .090 .066 .056 .065 .042 .041 .042 .041 .041 .042 .040
Std.
Deviation
1.090 1.455 1.074 .907 1.054 .681 .660 .674 .670 .673 .682 .650
Skewness -1.908 .217 -1.943 -1.233 .275 -.175 -.210 -.595 -.294 -.440 -.140 -.271
Std. Error of
Skewness
.150 .150 .150 .150 .150 .150 .150 .150 .150 .150 .150 .150
Kurtosis 3.525 -1.854 3.758 1.332 -.752 -.033 -.411 -.591 -.447 -.675 -.326 -.457
Std. Error of
Kurtosis
.299 .299 .299 .299 .299 .299 .299 .299 .299 .299 .299 .299
Table 4.6 Statistics of the distribution of EP in sugar mills of KP, Pakistan (n=263)
Figure 4.1 Histogram of distribution of HM of employees in sugar mills of KP, Pakistan.
EP1 EP2 EP3 EP4 EP5 EP6 EP7 EP8 EP9
Mean 6.0709 4.7255 4.8459 4.8462 4.7261 4.7069 4.6977 6.0798 5.9978
Std.
Deviation
.84481 .95336 .98771 .98912 .94962 .93238 .92232 .85523 .91482
Skewness -.562 .048 .010 .012 .051 .056 .064 -.580 -.550
Std. Error of
Skewness
.150 .150 .150 .150 .150 .150 .150 .150 .150
Kurtosis -.318 -.254 -.323 -.321 -.240 -.210 -.176 -.294 -.380
Std. Error of
Kurtosis
.299 .299 .299 .299 .299 .299 .299 .299 .299
55
Figure 4.2 Histogram of distribution of SM of employees in sugar mills of KP, Pakistan
Figure 4.3 Histogram of distribution of WM of employees in sugar mills of KP, Pakistan
56
Figure 4.4 Histogram of distribution of the JS of employees in sugar mills of KP,
Pakistan
Figure 4.5 Histogram of EP distribution of employees in sugar mills of KP, Pakistan.
Interpretation: The histograms of all the five research variables appear to be distributed
approximately normally, centered on respective mean values, although a few are somewhat
skewed. However moderate departures from normality with sample sizes larger than 50 is
little cause for concern.
57
4.2 Validity analysis
Repeat factor analysis was conducted using a principal component solution with rotation
method to determine the convergent validity of the scales. The rotation is an improvement as
it maximizes the fair loading of variables on their respective extracted factors. Each extracted
factor should have an Eigen value greater than 1.0. The extracted factors was then rotated
using orthogonal or oblique rotation techniques, depending on whether the underlying
constructs are expected to be relatively uncorrelated or correlated, to generate factor weights
that can be used to aggregate the individual items of each construct into a composite measure.
For adequate convergent validity, it is expected that items belonging to a common construct
should exhibit factor loadings of 0.60 or higher on a single/ same factor, while for
discriminant validity, these items should have factor loadings of 0.30 or less on all other
factors (cross-factor loadings). The factor loadings were from fair to excellent in present
research of all the scales as fair loadings are considered from 0.45-0.54, good from 0.55-0.62,
very good from 0.63-0.70, while over 0.71 are deemed excellent (Comrey, 1973). The results
of Validity of the Questionnaire are given as follows.
Table 4.7 KMO and Bartlett's Test for HM of sugar mill employees of KP, Pakistan
KMO and Bartlett's Test Component Matrix
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .661 Items Loadings
Bartlett's Test of Sphericity Approx. Chi-Square 174.056 hm1 .898
d.f. 10 hm2 .547
Sig. .000 hm3 .703
Required Computed Hm4 .395
KMO test > 0.7 .684 Hm5 .833
Bartlett’s test < 0.05 .000
Factor loading > than 0.4 -
Extraction Method: Principal Component Analysis. a. 1Components extracted.
Table 4.7a Communalities for HM of sugar mill employees of KP, Pakistan
Initial Extraction
HM 1.000 .613
HM 1.000 .399
HM 1.000 .554
HM 1.000 .1255
HM 1.000 .252
Table 4.7b Total Variance Explained for HM of sugar mill employees of KP, Pakistan
58
Component Initial Eigen values Extraction Sums of Squared Loadings
Total % of
Variance
Cumulative % Total % of
Variance
Cumulative %
1 2.072 41.448 41.448 2.072 41.448 41.448
2 .989 19.784 61.232
3 .789 15.784 77.016
4 .698 13.952 90.968
5 .452 9.032 100.000
Extraction Method: Principal Component Analysis.
Figure 4.6 Scree plot for HM of employees in sugar mills of KP, Pakistan
Table 4.7c Communalities for HM of sugar mill employees of KP, Pakistan
Component
HM .783
HM .631
HM .744
HM .505
HM .502
59
Tab 4.8 KMO & Bartlett's Test for SM measures of employees in sugar mills of KP,
Pakistan
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .747
Bartlett's Test of Sphericity Approx. Chi-Square 564.313
D.f. 10
Sig. .000
Tab 4.8a Communalities for SM of sugar mill employees of KP, Pakistan
Initial Extraction
SM 1.000 .761
SM 1.000 .228
SM 1.000 .751
SM 1.000 .622
SM 1.000 .552
Extraction Method: Principal Component Analysis.
Table 4.8b Total Variance Explained for SM of sugar mill employees of KP, Pakistan
Component Initial Eigen values Extraction Sums of Squared Loadings
Total % of
Variance
Cumulative % Total % of
Variance
Cumulative %
1 2.915 58.303 58.303 2.915 58.303 58.303
2 .914 18.272 76.575
3 .606 12.123 88.697
4 .380 7.595 96.292
5 .185 3.708 100.000
Extraction Method: Principal Component Analysis.
60
Figure 4.7 Scree plot for SM of employees of sugar mills of KP, Pakistan
Table 4.8c Component Matrix for SM of sugar mill employees of KP, Pakistan
Component
1
SM .872
SM .478
SM .867
SM .789
SM .743
Extraction Method: Principal Component Analysis. a. 1 components extracted.
Table 4.9 KMO & Bartlett's Test for WM measures of employees in sugar mills of KP,
Pakistan
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .641
Bartlett's Test of Sphericity Approx. Chi-Square 120.675
D.f. 10
Sig. .000
Table 4.9a Communalities for WM of sugar mill employees of KP, Pakistan
Initial Extraction
WM 1.000 .414
WM 1.000 .422
WM 1.000 .627
WM 1.000 .134
WM 1.000 .268
61
Table 4.9b Total Variance Explained for WM of sugar mill employees of KP, Pakistan
Component Initial Eigen values Extraction Sums of Squared
Loadings
Total % of Variance Cumula
tive %
Total % of
Variance
Cumulative
%
1 1.866 37.313 37.313 1.866 37.313 37.313
2 .999 19.988 57.301
3 .838 16.756 74.057
4 .785 15.696 89.753
5 .512 10.247 100.000
Extraction Method: Principal Component Analysis.
Figure 4.8 Scree plot for WM of employees of sugar mills of KP, Pakistan
Table 4.9c Component Matrix for WM of sugar mill employees of KP, Pakistan
Component
WM .644
WM .650
WM .792
WM .366
WM .517
Extraction Method: Principal Component Analysis. a. 1 components extracted.
Table 4.10 KMO & Bartlett's Test for JS of sugar mill employees of KP, Pakistan
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .839
Bartlett's Test of Sphericity Approx. Chi-Square 1515.418
D.f. 45
Sig. .000
62
Table 4.10a Communalities for JS of sugar mill employees of KP, Pakistan
Initial Extraction
JS1 1.000 .878
JS4 1.000 .926
JS5 1.000 .964
JS6 1.000 .733
JS7 1.000 .802
JS8 1.000 .990
JS9 1.000 .670
JS10 1.000 .987
JS11 1.000 .966
JS12 1.000 .909
Extraction Method: Principal Component Analysis.
Table 4.10b Total Variance Explained for JS of sugar mill employees of KP, Pakistan
Compone
nt
Initial Eigen values Extraction Sums of Squared
Loadings
Rotation Sums of Squared
Loadings
Total % of
Variance
Cumulati
ve %
Total % of
Variance
Cumula
tive %
Total % of
Variance
Cumula
tive %
1 4.944 49.438 49.438 4.944 49.438 49.438 3.280 32.802 32.802
2 1.014 10.135 59.573 1.014 10.135 59.573 1.236 12.357 45.159
3 .904 9.039 68.612 .904 9.039 68.612 1.114 11.143 56.303
4 .769 7.689 76.301 .769 7.689 76.301 1.091 10.905 67.208
5 .655 6.553 82.854 .655 6.553 82.854 1.069 10.690 77.898
6 .539 5.391 88.245 .539 5.391 88.245 1.035 10.347 88.245
7 .492 4.915 93.160
8 .399 3.988 97.148
9 .236 2.359 99.508
10 .049 .492 100.000
63
Figure 4.9 Scree plot for job satisfaction of employees of sugar mills of KP, Pakistan
Table 4.10c Rotated Component Matrix for JS of sugar mill employees of KP,
Pakistan
Rotated Component Matrixa
Component
1 2 3 4 5
JS1 .850
JS7 .798
JS12 .797
JS3 .704
JS11 .656
JS6 .636
JS9 .558
JS5 -.837
JS2 .761
JS10 .883
JS4 .875
JS8 .962
Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser
Normalization. a. Rotation converged in 9 iterations.
Table 4.11 KMO & Bartlett's Test for EP of sugar mill employees of KP, Pakistan
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .749
Bartlett's Test of Sphericity Approx. Chi-Square 999.222
D.f. 36
Sig. .000
64
Table 4.11a Communalities for EP of sugar mill employees of KP, Pakistan
Initial Extraction
EP1 1.000 .896
EP2 1.000 .820
EP3 1.000 .794
EP4 1.000 .794
EP5 1.000 .756
EP6 1.000 .736
EP7 1.000 .900
EP8 1.000 .765
EP9 1.000 .822
Extraction Method: Principal Component Analysis.
Table 4.11b Total Variance Explained for EP of sugar mill employees of KP, Pakistan
Comp
onent
Initial Eigen values Extraction Sums of Squared
Loadings
Rotation Sums of Squared
Loadings
Total % of
Variance
Cumulative
% Total
% of
Variance
Cumul
ative
%
Total % of
Variance
Cumulative
%
1 3.253 36.147 36.147 3.253 36.147 36.147 2.478 27.528 27.528
2 2.211 24.565 60.712 2.211 24.565 60.712 1.870 20.778 48.306
3 .997 11.075 71.788 .997 11.075 71.788 1.618 17.980 66.286
4 .821 9.126 80.913 .821 9.126 80.913 1.316 14.627 80.913
5 .430 4.781 85.694
6 .405 4.497 90.191
7 .375 4.168 94.359
8 .355 3.945 98.304
9 .153 1.696 100.000
65
Figure 4.10 Scree plot for EP of employees of sugar mills of KP, Pakistan
Table 4.11c Component Matrix for EP of sugar mill employees of KP, Pakistan
Rotated Component Matrixa
Component
1 2 3 4
EP .939
EP .894
EP .857
EP .889
EP .789
EP .860
EP .856
EP .932
EP .515 .646
Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser
Normalization. a. Rotation converged in 5 iterations.
Interpretation
Factor analysis was employed to test the construct validity to ensure that the set of
items represents a sole construct (i.e., convergent validity). All scales were subjected to factor
analysis of responses to the questionnaires (n =263) using principal component solution with
a varimax rotation method to improve the interpretability of factors through rotation. The
minimum recommended factor loading for EFA is 0.40. The factor loadings of all the scales
were noted almost excellent i.e., above 0.71 and almost all the scales items loaded exactly on
66
their respective factor constructs with the exception of only 4 items, 1 each of HM, SM, WM
and JS, that were loaded differently stating sound validity of instrument of present study.
KMO value for HM was recorded as 0.684 which is > 0.5. It means our sample size is
sufficient for EFA. BTS value in this case is statistically significant as < 0.001. It means our
data is reliable for factor analysis. Factor loading recorded was > 0.4 for all the items. It
means there is 1 factor of HM is lying in the questionnaire. For HM, when items number 1-5
of the instrument were subjected to FA by using a principal component solution with a
varimax rotation, one factors having eigen value greater than 1 was explored conforming to
respective constructs for HM.
KMO value for SM was recorded as 0.747 which is > 0.5. It means our sample size is
sufficient for exploratory factor analysis. BTS value is statistically significant as < 0.001. It
means our data is reliable for factor analysis. Factor loading recorded was > 0.4 for all the
items. It means there is 1 factor of SM lying in the questionnaire. For SM, when items
number 6-10 of the instrument were subjected to FA by using a principal component solution
with a varimax rotation, one factors having eigen value greater than 1 was explored
conforming to respective constructs for SM.
KMO value for WM was recorded as 0.641 which is > 0.5. It means our sample size is
sufficient for exploratory factor analysis. BTS value in this case is statistically significant as
< 0.001. It means our data is reliable for factor analysis. Factor loading recorded was > 0.4
for all the items. It means there is 1 factor of WM lying in the questionnaire. For WM, when
items number 11-15 of the instrument were subjected to FA by using a principal component
solution with a varimax rotation, one factors having eigen value greater than 1 was explored
conforming to respective constructs for WM.
KMO value for job satisfaction was recorded as 0.839 which is > 0.5. It means our sample
size is sufficient for exploratory factor analysis. BTS value is statistically significant as <
0.05. It means our data is reliable for factor analysis. Factor loading recorded tells there were
six factors of JS lying in the questionnaire i.e pay, promotion, colleagues, supervisors, work
and work environment. The varimax rotation (orthogonal) was used in this study because the
major objective of varimax rotation is to have a factor structure in which each variable loads
highly on one and only one factor (Mishra, 2013). For JS, when items number 16-27 of the
67
instrument were subjected to FA by using a principal component solution with a varimax
rotation, five factors having eigen value greater than 1 and 0.7 were explored conforming to
respective constructs for JS. All the factors extracted with two items each anticipated to
measure pay, promotion, co-workers, supervisor, colleagues, work and work environment
loaded onto their respective factors with all showing very good loadings.
KMO value for employee performance was recorded as 0.749 which is > 0.5. It means our
sample size is sufficient for exploratory factor analysis. BTS value is statistically significant
as < 0.05. It means our data is reliable for factor analysis. Factor loading recorded tells there
were four factors of EP lying in the questionnaire. Scree plot also tells the same. Varimax
rotation with fixed number of factors at 4 validates 4 factors i.e. efficiency, effectiveness,
responsiveness and innovativeness. The varimax rotation (orthogonal) was used in this study
because the major objective of varimax rotation is to have a factor structure in which each
variable loads highly on one and only one factor. For EP, when items number 30-38 of the
instrument were subjected to FA by using a principal component solution with a varimax
rotation, four factors having eigenvalue greater than 1 were explored conforming to
respective constructs for EP. The first factor extracted with two items anticipated to measure
‘efficiency’ loaded onto this factor. The second factor with the next two items intended to
measure ‘effectiveness’ loaded onto this factor. The third with next two and fourth with last
three items intended to measure ‘responsiveness’ and ‘innovativeness’ loaded over their
respective factors and all showing very good loadings.
68
4.3 Reliability Analysis
Table 4.12 Reliability Statistics for HM of employees of sugar mills of KP, Pakistan
Cronbach's Alpha N of Items
.636 5
Table 4.12a Item-Total Statistics for HM of employees of sugar mills of KP, Pakistan
Scale Mean if Item
Deleted
Scale Variance if
Item Deleted
Corrected Item-
Total Correlation
Cronbach's Alpha
if Item Deleted
HM 22.38 6.565 .532 .500
HM 22.41 7.739 .384 .591
HM 22.11 8.019 .499 .530
HM 22.13 9.316 .281 .631
HM 23.86 10.373 .296 .628
Table 4.13 Reliability Statistics for SM of employees of sugar mills of KP, Pakistan
Cronbach's Alpha N of Items
.806 5
Table 4.13a Item-Total Statistics for SM of employees of sugar mills of KP, Pakistan
Scale Mean if Item
Deleted
Scale Variance if
Item Deleted
Corrected Item-
Total Correlation
Cronbach's Alpha
if Item Deleted
SM 23.96 11.017 .751 .712
SM 23.99 14.637 .341 .846
SM 23.90 11.682 .750 .715
SM 23.69 13.834 .618 .763
SM 23.65 14.357 .556 .780
Table 4.14 Reliability Statistics for WM of employees of sugar mills of KP, Pakistan
Cronbach's Alpha N of Items
.550 5
69
Table 4.14a Item-Total Statistics for WM of employees of sugar mills of KP, Pakistan
Scale Mean if Item
Deleted
Scale Variance if
Item Deleted
Corrected Item-
Total Correlation
Cronbach's Alpha if
Item Deleted
WM 24.08 6.181 .335 .480
WM 24.10 6.380 .318 .491
WM 23.95 6.154 .481 .407
WM 24.29 6.925 .190 .565
WM 24.35 6.359 .274 .519
Since our alpha is 0.550, we don’t need to delete any items, since the ITC values are > 0.4,
the cut-off value.
Table 4.14b Combined reliability of OHS Item-Total Statistics
Scale Mean if
Item Deleted
Scale Variance
if Item Deleted
Corrected Item-
Total Correlation
Cronbach's Alpha if
Item Deleted
Alpha
HM 82.37 76.983 .766 .852
HM 82.40 83.432 .512 .867
HM 82.10 84.082 .614 .862
HM 82.12 88.135 .410 .871
HM 83.85 91.966 .358 .872
SM 82.37 76.983 .766 .852
SM 82.40 83.432 .512 .867 0.874
SM 82.32 80.211 .684 .857
SM 82.10 85.467 .552 .865
SM 82.07 85.430 .568 .864
WM 82.10 84.082 .614 .862
WM 82.12 88.135 .410 .871
WM 81.97 87.480 .529 .866
WM 82.31 91.475 .222 .879
WM 82.37 89.609 .300 .876
Table: 4.15 Reliability Statistics for JS of employees of sugar mills of KP, Pakistan
Cronbach's Alpha N of Items
.914 12
70
Table 4.15a Item-Total Statistics for JS of employees of sugar mills of KP, Pakistan
Scale Mean if Item
Deleted
Scale Variance if
Item Deleted
Corrected Item-
Total Correlation
Cronbach's Alpha
if Item Deleted
JS 48.03 26.640 .878 .896
JS 48.04 27.281 .768 .901
JS 48.02 27.477 .747 .902
JS 48.03 28.522 .547 .911
JS 48.01 28.118 .604 .909
JS 48.03 27.350 .704 .904
JS 48.04 27.006 .804 .900
JS 48.00 31.179 .176 .927
JS 48.02 27.370 .733 .903
JS 48.06 29.222 .451 .915
JS 48.01 28.118 .604 .909
JS 48.03 26.640 .878 .896
Table 4.16 Reliability Statistics for EP of employees of sugar mills of KP, Pakistan
Cronbach's Alpha N of Items
.776 9
Table 4.16a Item-Total Statistics for EP of employees of sugar mills of KP, Pakistan
Scale Mean if
Item Deleted
Scale Variance if
Item Deleted
Corrected Item-
Total Correlation
Cronbach's Alpha
if Item Deleted
EP1 40.6259 20.486 .498 .749
EP2 41.9713 20.074 .471 .752
EP3 41.8509 19.859 .474 .752
EP4 41.8505 19.773 .484 .750
EP5 41.9706 19.366 .567 .738
EP6 41.9899 19.767 .527 .744
EP7 41.9991 21.210 .347 .770
EP8 40.6169 21.266 .381 .765
EP9 40.6989 21.009 .377 .766
71
Interpretation:
Cronbach’s alpha is a useful and accepted technique for internal consistency of scales of
measurement. Tables 4.12 to 4.16a present the summary of calculated coefficient alphas for
each of the 5 items of the HM, SM and WM, 12 of JS and 9 of EP, showing that all
coefficient alpha values for the total items and for each scale ranged from 0.550 to 0.915 and
are in the acceptable range, which proves sound reliability of the instrument.
4.4 Descriptive Statistics
The section is divided into two parts; descriptive and inferential. The descriptive portion
presents the frequencies and percentages for all demographic and mean values and standard
deviations for research variables of the sample. The frequency and percentage are computed
for Age, Residence, Education and Experience for understanding the characteristics of the
respondents. The mean and standard deviation are computed for all the research variables.
Subsequently testing of the hypotheses as final analyses with appropriate statistical tests to
answer the respective research questions was done.
a. Frequency distribution of demographic variables
AGE: 25 (9.5%) were 19-40 & 238 (90.5%) were 41-60 years.
RES: 93 (35.4%) were Urban and 170 (64.6) were Rural.
EDU: 90 (34.2%) were up to 5, 83 (31.6%) 6-10 & 90 (34.2%) > 10 yrs.
EXP: 133 (50.6%) were up to 5 & 130 (49.4%) > 5 years.
Table 4.17 Frequencies of age-groups of sugar mill employees, KP, Pakistan (n=263)
Frequency Percent Cumulative Percent
Valid 19-40 25 9.5 9.5
41-60 238 90.5 100.0
Total 263 100.0
Table 4.18 Frequencies of residence of sugar mill employees, KP, Pakistan (n=263)
Frequency Percent Cumulative Percent
Valid Urban 93 35.4 35.4
Rural 170 64.6 100.0
Total 263 100.0
72
Table 4.19 Frequencies of education of sugar mill employees, KP, Pakistan (n=263)
Frequency Percent Cumulative Percent
Valid Primary 90 34.2 34.2
Secondary 83 31.6 65.8
Higher 90 34.2 100.0
Total 263 100.0
Table 4. 20 Frequencies of experience of sugar mill employees, KP, Pakistan (n=263)
Frequency Percent Cumulative
Percent
Valid up to 5 yr 133 50.6 50.6
> 5 yr 130 49.4 100.0
Total 263 100.0
Interpretation
Demographic profile indicates that majority of respondents have age between 41-60 years,
are rural, with education up to 5 and >10 years and experience up to 5 years.
b. Descriptive analysis of research variables
The measures of central tendency and dispersion for all five research/ numeric variables are
placed in the following table.
Table 4.21 Descriptive analysis of research variables (n=263)
Minimum Maximum Mean Std.
Deviation
Health Measures 3.00 6.60 5.6441 .69383
Safety Measures 2.60 7.00 5.9597 .88381
Welfare Measures 3.80 7.00 6.0388 .59963
Job Satisfaction 3.17 5.08 4.3660 .47840
Employees performance 3.33 6.67 5.1884 .61112
73
4.5 Inferential statistics (Testing of hypothesis)
4.5.1 Correlation analysis of employees of sugar mills of KP, Pakistan
HA1. EP is statistically significantly & positively correlated with HM, SM, WM & JS.
Table 4.22 Correlations
HM SM WM JS EP
HM Pearson Correlation 1 .841** .754** .493** .533**
Sig. (2-tailed) .000 .000 .000 .000
SM Pearson Correlation .841** 1 .559** .347** .365**
Sig. (2-tailed) .000 .000 .000 .000
WM Pearson Correlation .754** .559** 1 .454** .477**
Sig. (2-tailed) .000 .000 .000 .000
JS Pearson Correlation .493** .347** .454** 1 .540**
Sig. (2-tailed) .000 .000 .000 .000
EP Pearson Correlation .533** .365** .477** .540** 1
Sig. (2-tailed) .000 .000 .000 .000
N 263 263 263 263 263
**. Correlation is significant at the 0.01 level (2-tailed).
Interpretation
Since, the data was on interval scale and normally distributed, thus for H1 testing, Pearson
correlation test was used. As the last two rows in table 4.22 reveal that, EP and HM (r = .533,
p<.001), EP and SM (r = .365, p<.001), EP and WM (r = .477, p<.001) and EP and JS (r =
.540, p<.001) which indicates a medium to strong positive relationship: the higher the levels
of HM, SM, WM and JS, the higher the level of EP tends to be. Values of + 0.1 shows small
effect, + 0.3 shows medium effect and + 0.5 is a large effect. The second line shows the
probability that the correlation occurred by chance, only in <1 out of 1000 samples of 263
respondents. Therefore H1was accepted. Assumptions include:
Table 4.22a Assumptions of Multiple Linear Correlation
1. Linearity The relationship if it exists is best regarded as linear
2. Variable type The two variables are continuous
3. Distribution The two variables are normally distributed (at least
symmetric by histogram)
4. How to check The linearity assumption is checked by producing a two-
way scatter plot of the two variables
74
4.5.2 Regression analysis
HA2. EP is predicted by HM, SM, WM & JS.
Table 4.23: Model Summary of sugar mill employees of KP, Pakistan (n=263)
Model Summary
Model R R Square Adjusted R Square Std. Error of the
Estimate
1 .632a .400 .390 .47712
a. Predictors: (Constant), JS, SM, WM, HM
As we know standard deviation is the average deviation of values from the mean in a sample,
whereas SE shows the deviation of individual values from the mean. Here the standard
error of the estimate tells the wrongness of the regression model using the units of the
criterion. It represents the average distance of the observed values from the regression line.
Table 4.23a: ANOVA
Model Sum of
Squares
D.f. Mean Square F Sig.
1 Regression 39.116 4 9.779 42.959 .000a
Residual 58.731 258 .228
Total 97.848 262
a. Predictors: (Constant), JS, SM, WM, HM
b. Dependent Variable: EP
Table 4.23b: Coefficients of Regression
Model Unstandardized Coefficients Standardized
Coefficients
t Sig.
B Std. Error Beta
1 (Constant) 1.278 .338 3.777 .000
HM .408 .105 .463 3.893 .000
SM -.128 .063 -.186 -2.021 .044
WM .077 .077 .076 1.001 .318
JS .437 .072 .342 6.059 .000
a. Dependent Variable: EP
75
Interpretation
Simple regression predicts values of one variable from the other, using R2 (the proportion of
variance in outcome explained by the model) and F value (ratio of goodness of the model
versus badness in explaining the variance). Significant p-value means predictor significantly
predicts outcome. Descriptive statistics should be used to check the correlation matrix for
multi-collinearity. The correlation among predictors must be lower than 0.90.
The table 4.23 shows that R2 = 0.400 is the proportion of the variance in EP accounted for by
the four independent variables. R2 (Coefficient of determination) =0.400 indicates 40 % of the
variance in EP is accounted for by the four independent variables, therefore it is proved that
independent variables contribute positively towards change in the dependent variable. While
still leaves 60% unexplained i.e. additional variables important in explaining EP have not
been considered in this study. The level of significance is <0.0001 (Table 4.23a). R2 is
actually the square of correlation r=0.632. The p-values of predictors are far less than the
alpha of 0.05 except WM, lead us to reject the null hypothesis, therefore the H2 was accepted
as true.
Table 4.23b presents an estimate of the intercept (or constant) equal to approximately 1.278
and the slope coefficient. The constant as the average expected value of the dependent
variable when the independent variable equals zero which can never be zero, so the constant
does not receive attention. Assumptions of regression to make sure the model generalizes
beyond sample are checked. The graph looking like a random array of dots versus a funnel is
good. Histograms show up as normal distributions and the P–P plot looks like a diagonal line
versus snaky line.
Furthermore, table 4.23b, presents the statistics on the role of predictors (HM, SM, WM and
JS) in terms of beta values. The estimated value between HM to EP of 0.408 (slope
coefficient) represents the average marginal effect of HM on EP and interpreted as the
expected change in the EP on average for a one-unit increase in the HM; every increase in
HM of 1 unit SD is associated with an average increase in the EP of .408 units SDs,
controlling for the effect of the other IVs. If SM increases by one unit, the EP will increase at
sugar mills KP by .128 units respectively (negative value coming as anomaly probably due to
erroneous data coming from employees which will have to be accepted). WM gives non-
76
significant result with p-value greater than the required threshold of 0.05. If JS increases by
one unit, the EP will increase at sugar mills KP by .437 units. Hence becomes an excellent
decision making tool. For predictors measured in different units we use standardized beta
coefficients by standardizing variables i.e. mean=0 and SD=1. If R2 were equal to 1, all
variance in EP will be explainable by the predictors and regression model will fit the data
well and vice versa. The estimate is statistically significantly different from zero. This leads
us to reject the H0. There does appear a positive relationship between HM, SM and WM and
EP across sugar mills in KP, Pakistan. The regression equation to predict EP is therefore as
follows:
EP=1.278+0.408HM+0.128SM+0.077WM+.437JS.
The assumptions for linear multiple regressions (Tabachnick & Fidell, 2007) are:
Table 4.24 Assumptions of Multiple Linear Regression
1. Type of variable All predictors and the criterion must be
quantitative, continuous
2. Sample Size Should be large
3. Multi-collinearity Predictors should not be highly correlated
4. Linearity There should be no curvilinear effect
Figure 4.11 Normal P-P plot of regression standardized residual for predictors &
criterion of employees of sugar mills of KP, Pakistan.
4.5.3 Mediation analysis
77
Mediatory variable plays a supporting role. Alone an independent variable cannot explain a
dependent variable. It must act on and operate through mediation or intervening variable.
Mediating variable surfaces at time t2 as a help to understand how IV at t1 affects the DV at
t3. It doesn’t add to variance already explained by IV in DV. Hierarchical regression analysis
tells the improvement of the model by looking at the change in R2 and whether significant or
not (Sig. F Change). The ANOVA also tells us whether the model is a significant fit of the
data overall. Mediating or intermediate variables are explained by predictors while also
explaining outcomes. The Coefficients table tells the individual contribution of predictors to
the regression model in a hierarchical regression. Final model shows for each predictor, if it
has made a significant contribution to predicting the outcome. Also look at the standardized
beta values because these tell the importance of each predictor (bigger absolute value = more
important).
We applied Baron and Kenny’s (1986) strategy for testing mediating effect of JS. Two pre-
conditions (logical) and two conditions (decisional) must be met to confirm the presence of
mediation effect. Firstly, the predictor variable or X must have a significant effect on the
mediator variable or M (Path a through simple regression). Secondly, the predictor variable
must have a significant effect on the dependent variable (Path c through simple regression).
As a result of multiple regression by putting the X as well as M simultaneously in a
hierarchical manner after path c, the mediator variable or M must have a significant effect on
the dependent variable which shows the presence of mediation effect (Path b or third
decisional condition) evident in the form of R2 change and finally, the c prime (fourth
decisional condition) will decide whether there is full mediation (insignificant c prime) or
partial mediation (significant c prime). This means the mediator has strengthened the already
existing relationship between X and Y.
HA3. JS strengthens the relationship b/w EP & HM (n=263)
78
The potential mediating role of JS was
examined by conducting step-wise
multiple regressions (Baron and Kenny,
1986) with HM as the independent
variable, JS as the potential mediator and
EP as the dependent variable.
Figure 4.12 Mediation Model 1 of employees of sugar mills of KP, Pakistan.
Table 4.25 Model Summary
Model R R
Square
Adjusted
R
Square
Std.
Error of
the
Estimate
Change Statistics
R
Square
Change
F
Change
d.f.1 d.f.2 Sig. F
Change
1 .533a .284 .281 .51813 .284 103.474 1 261 .000
2 .621b .386 .381 .48087 .102 43.022 1 260 .000
Table 4.25a ANOVA
Model Sum of
Squares
D.f. Mean Square F Sig.
1 Regression 27.779 1 27.779 103.474 .000b
Residual 70.069 261 .268
Total 97.848 262
2 Regression 37.727 2 18.863 81.578 .000c
Residual 60.121 260 .231
Total 97.848 262
Table 4.25b Coefficients
Model Unstandardized Coefficients Standardized
Coefficients
T Sig.
B Std. Error Beta
1 (Constant) 2.540 .262 9.680 .000
HM .469 .046 .533 10.172 .000
2 (Constant) 1.394 .300 4.650 .000
HM .310 .049 .352 6.305 .000
JS .468 .071 .366 6.559 .000
a. Dependent Variable: EP; b. Predictors: (Constant), HM; c. Predictors: (Constant), HM, JS
Interpretation
79
Path ‘a’ was significant. Therefore, condition one was supported. Path ‘c’ before the
inclusion of the mediator (simple regression with one IV & I DV) with significant ANOVA
was (p=0.000) with R2=0.284 and un-standardized beta weight=0.469 contributing 28%
variance in the EP. After path ‘c’, multiple regression was run by putting JS along with HM
simultaneously in a hierarchical manner. Path ‘b’ was found to be significant meaning
mediation is there. Path c prime was significant (p=0.000) with R2=0.386 and un-
standardized beta =0.310 i.e. R2 has gone up from 0.284 (path c) to 0.386 (path c prime)
showing an improvement of 10.2% in the variance of the HM (R2 change = 0.102) in EP due
to JS. Similarly the beta value has gone down from 0.469 (path c) to 0.310 (path c prime)
showing that the mediator variable has affected the dependent variable. The mediator has
therefore strengthened the relationship between predictor HM and criterion EP and acting as
partially mediating the relationship between HM and EP. According to our research, H3 was
accepted (Figure 4.12).
HA4. JS strengthens the relationship b/w EP & SM (n=263)
The potential mediating role of JS was
examined by conducting step-wise
multiple regressions (Baron and Kenny,
1986) with SM as the independent
variable, JS as the potential mediator and
EP as the dependent variable.
Figure 4.13 Mediation Model 2 of employees of sugar mills of KP, Pakistan.
Table 4.26 Model Summary
Model R R2 Adjusted
R2
Std.
Error
Change Statistics
R2 F d.f.1 d.f.2 Sig. F
1 .365a .133 .130 .57002 .133 40.140 1 261 .000
2 .572b .327 .322 .50308 .194 75.075 1 260 .000
Table 4.26a ANOVA
80
Model Sum of
Squares
D.f. Mean Square F Sig.
1 Regression 13.042 1 13.042 40.140 .000b
Residual 84.805 261 .325
Total 97.848 262
2 Regression 32.043 2 16.022 63.303 .000c
Residual 65.804 260 .253
Total 97.848 262
Table 4.26b Coefficients of regression of employees of sugar mills of KP, Pakistan
Model Unstandardized
Coefficients
Standardize
d
Coefficients
T Sig.
B Std. Error Beta
1 (Constant) 3.684 .240 15.346 .000
SM .252 .040 .365 6.336 .000
2 (Constant) 1.736 .309 5.617 .000
SM .140 .037 .202 3.724 .000
JS .600 .069 .470 8.665 .000
a. Dependent Variable: EP
Interpretation
Path ‘a’ was significant. Therefore, condition one was supported. Path ‘c’ before the
inclusion of the mediator (simple regression with one IV & I DV) with significant ANOVA
was significant (p=0.000) with R2=.133 and un-standardized beta weight=0.252 contributing
13% variance in the EP. After path ‘c’, multiple regression was run by putting JS along with
SM simultaneously in a hierarchical manner. Path ‘b’ was found to be significant meaning
mediation is there. Path c prime was significant (p=0.000) with R2=0.327 and un-
standardized beta =0.140 i.e. R2 has gone up from 0.133 (path c) to 0.327 (path c prime)
showing an improvement of 19.4% in the variance of the HM (R2 change = 0.192) in EP due
to JS. Similarly the beta value has gone down from 0.252 (path c) to 0.140 (path c prime)
showing that the mediator variable has affected the dependent variable. The mediator has
therefore strengthened the relationship between predictor SM and criterion EP and acting as
partially mediating the relationship between SM and EP. According to our research, H4 was
accepted (Figure 4.13).
HA3. JS strengthens the relationship b/w EP & WM (n=263)
81
The potential mediating role of JS was
examined by conducting step-wise
multiple regressions (Baron and Kenny,
1986) with HM as the independent
variable, JS as the potential mediator
and EP as the dependent variable.
Figure 4.14 Mediation Model 3 of employees of sugar mills of KP, Pakistan.
Table 4.27 Model Summary
Model R R
Square
Adjusted
R2
Std.
Error
Change Statistics
R2 F d.f.1 d.f.2 Sig. F
1 .477a .227 .224 .53824 .227 76.750 1 261 .000
2 .599b .359 .354 .49111 .132 53.495 1 260 .000
Table 4.27a ANOVA
Model Sum of Squares D.f. Mean Square F Sig.
1 Regression 22.235 1 22.235 76.750 .000b
Residual 75.613 261 .290
Total 97.848 262
2 Regression 35.137 2 17.569 72.841 .000c
Residual 62.710 260 .241
Total 97.848 262
Table 4.27b Coefficients
Model Unstandardized Coefficients Standardized
Coefficients
T Sig.
B Std. Error Beta
1 (Constant) 2.255 .337 6.700 .000
WM .486 .055 .477 8.761 .000
2 (Constant) 1.121 .344 3.258 .001
WM .297 .057 .292 5.232 .000
JS .521 .071 .408 7.314 .000
a. Dependent Var: EP; b. Predictors: (Constant), WM; c. Predictors: (Constant), WM, JS
Interpretation
Path ‘a’ was significant. Therefore, condition one was supported. Path ‘c’ before the
inclusion of the mediator (simple regression with one IV & I DV) with significant ANOVA
was significant (p=0.000) with R2=0.227 and un-standardized beta weight=0.486 contributing
23% variance in the EP. After path ‘c’, multiple regression was run by putting JS along with
WM simultaneously in a hierarchical manner. Path ‘b’ was found to be significant meaning
82
mediation is there. Path c prime was significant (p=0.000) with R2=0.354 and un-
standardized beta =0.297 i.e. R2 has gone up from 0.227 (path c) to 0.354 (path c prime)
showing an improvement of 13.2% in the variance of the WM (R2 change = 0.132) in EP due
to JS. Similarly the beta value has gone down from 0.486 (path c) to 0.297 (path c prime)
showing that the mediator variable has affected the dependent variable. The mediator has
therefore strengthened the relationship between predictor HM and criterion EP and acting as
partially mediating the relationship between HM and EP. According to our research, H5 was
accepted (Figure 4.14).
4.5.4 Tests of significance
a. Age
HA6. Older employees score higher than Youngers on 5 RVs
Table 4.28: Descriptive-data on Age-groups
Age N Mean Std. Deviation Std. Error Mean
HM 19-40 25 4.9920 .77348 .15470
41-60 238 5.7126 .64964 .04211
SM 19-40 25 5.4480 1.09435 .21887
41-60 238 6.0134 .84363 .05468
WM 19-40 25 5.4160 .82446 .16489
41-60 238 6.1042 .53239 .03451
JS 19-40 25 3.8933 .39493 .07899
41-60 238 4.4156 .45954 .02979
EP 19-40 25 4.1156 .33866 .06773
41-60 238 5.3011 .51679 .03350
Table 4.28a: Independent Samples Test
F Sig. T d.f. Sig. (2-tailed)
HM 1.624 .204 5.178 261 .042
SM 6.125 .014 3.092 261 019
WM 9.173 .003 5.788 261 .000
JS 5.688 .018 5.472 261 .010
EP 6.237 .013 11.210 261 .000
Interpretation
Homogeneity of variance is an important assumption means that the variances should be the
same throughout the data. As in the table 4.34a, Levene’s Test for Equality of Variances
reports non-significant values; it means assumption of homogeneity of variances is not
violated. Accordingly, first row ‘Equal variances assumed’ (EVA) has been considered for all
83
research variables. Table 4.34a gives the results of independent samples t-test application on
the mean differences between the age 19-40 and 41-60 years respondents on all five research
variables. The t-value, degree of freedom and significance level are presented. The t-value
and its associated level of significance lead us to reject the null hypothesis of no difference.
For H6 substantiation, with p-value of <0.0001, the null hypothesis was rejected at 0.05, so
the difference between the mean scores between the age 19-40 and 41-60 years was
statistically significant on all research variables.
b. Residence
HA7. Urban employees score higher than rural on 5 RVs
Table 4.29 Group Statistics
Residence N Mean Std. Deviation Std. Error Mean
HM Urban 93 5.2602 .68575 .07111
Rural 170 5.8541 .60404 .04633
SM Urban 93 5.6258 .93494 .09695
Rural 170 6.1424 .80013 .06137
WM Urban 93 5.7204 .62841 .06516
Rural 170 6.2129 .50613 .03882
JS Urban 93 4.0681 .37830 .03923
Rural 170 4.5289 .44874 .03442
EP Urban 93 4.5496 .36388 .03773
Rural 170 5.5379 .39931 .03063
Table 4.29a Independent Samples t-Test
F Sig. T d.f. Sig. (2-tailed)
HM .206 .651 -7.262 261 .070
SM 1.395 .239 -4.711 261 .060
WM .734 .392 -6.914 261 .090
JS 27.807 .000 -8.402 261 .150
EP 1.414 .236 -19.791 261 .090
Interpretation
As in the table 4.35a, Levene’s Test for Equality of Variances reports non-significant values
it means assumption of homogeneity of variances is not violated. Accordingly, first row
‘Equal variances assumed’ (EVA) has been considered for all research variables. The t-value,
d.f. and significance level are presented. The t-value and its associated level of significance
lead us to accept the null hypothesis of no difference at 0.05, so the difference between the
84
mean scores between the urban and rural respondents was statistically non-significant on all
research variables.
c. Education
HA8: There are significant demographic group mean differences of Education on all five
research variables.
Table 4.30 Descriptive data on Education
N Mean Std. D Std. Error 95% Confidence
Interval for Mean
Min Max
Lower Upper
HM Up to 5
year
90 5.4644 .69465 .07322 5.3190 5.6099 3.00 6.60
6-10
year
83 5.4265 .60225 .06611 5.2950 5.5580 3.80 6.60
> 10
year
90 6.0244 .61431 .06475 5.8958 6.1531 4.20 6.60
Total 263 5.6441 .69383 .04278 5.5599 5.7284 3.00 6.60
SM Up to 5
year
90 5.8178 .90176 .09505 5.6289 6.0066 3.00 7.00
6-10
year
83 5.7855 .84321 .09255 5.6014 5.9697 2.60 7.00
> 10
year
90 6.2622 .83067 .08756 6.0882 6.4362 3.80 7.00
Total 263 5.9597 .88381 .05450 5.8524 6.0670 2.60 7.00
W
M
Up to 5
year
90 5.9422 .62260 .06563 5.8118 6.0726 3.80 7.00
6-10
year
83 5.8337 .56897 .06245 5.7095 5.9580 3.80 7.00
> 10
year
90 6.3244 .49134 .05179 6.2215 6.4274 5.00 7.00
Total 263 6.0388 .59963 .03697 5.9660 6.1116 3.80 7.00
JS Up to 5
year
90 4.2009 .46806 .04934 4.1029 4.2990 3.17 5.00
6-10
year
83 4.2269 .41957 .04605 4.1353 4.3185 3.17 5.08
> 10
year
90 4.6593 .39988 .04215 4.5755 4.7430 3.33 5.08
Total 263 4.3660 .47840 .02950 4.3079 4.4241 3.17 5.08
EP Up to 5
year
90 4.8235 .49994 .05270 4.7187 4.9282 3.33 6.44
6-10
year
83 4.9050 .40142 .04406 4.8173 4.9926 3.67 5.44
> 10
year
90 5.8148 .30758 .03242 5.7504 5.8792 5.11 6.67
Total 263 5.1884 .61112 .03768 5.1142 5.2626 3.33 6.67
Table 4.30a ANOVA
85
Sum of
Squares
D.f. Mean Square F Sig.
HM Between Groups 19.854 2 9.927 24.287 .000
Within Groups 106.274 260 .409
Total 126.128 262
SM Between Groups 12.567 2 6.284 8.505 .000
Within Groups 192.086 260 .739
Total 204.653 262
WM Between Groups 11.673 2 5.837 18.387 .000
Within Groups 82.531 260 .317
Total 94.204 262
JS Between Groups 11.798 2 5.899 31.845 .000
Within Groups 48.165 260 .185
Total 59.963 262
EP Between Groups 53.971 2 26.985 159.905 .000
Within Groups 43.877 260 .169
Total 97.848 262
Interpretation
This method tests whether the mean values of continuous variables (Research variables)
differ across two or more subgroups of the data defined as a categorical variable (Education)
to explore whether they are related (associated) to each other. Table 4.36 presents between,
within and total sums of squares, with their degrees of freedom. The Mean squares are the
respective sums of squares divided by the degrees of freedom. The F-test is the Mean Square
between groups divided by the Mean Square within groups. As the significance level on all
research variables is lower than 0.0, it would lead us to reject the null hypothesis of no
difference between the three education groups. We conclude that there are statistically
significant differences between them. Thus H8 stands accepted. As education proves to
distinguish between research variables of workers, the variance in research variables within
educational groups will be small relative to the variance across all groups. Pair-wise
differences need additional analysis, hence post hoc Tukey HSD test was conducted.
Table 4.30b Tukey HSD Results of Multiple Comparisons of Different Education
Groups
Dependent
Variable
(I)
Educati
on
(J)
Education
Mean
Differen
ce (I-J)
Std. Error Sig. 95% Confidence
Interval
Lower
Bound
Upper
Bound
86
HM Up to 5
year
6-10 year .03794 .09729 .920 -.1914 .2673
> 10 year -.56000* .09531 .000 -.7847 -.3353
6-10
year
Up to 5 year -.03794 .09729 .920 -.2673 .1914
> 10 year -
.59794*
.09729 .000 -.8273 -.3686
> 10
year
Up to 5 year .56000* .09531 .000 .3353 .7847
6-10 year .59794* .09729 .000 .3686 .8273
SM Up to 5
year
6-10 year .03224 .13080 .967 -.2761 .3406
> 10 year -.44444* .12813 .002 -.7465 -.1424
6-10
year
Up to 5 year -.03224 .13080 .967 -.3406 .2761
> 10 year -.47668* .13080 .001 -.7850 -.1683
> 10
year
Up to 5 year .44444* .12813 .002 .1424 .7465
6-10 year .47668* .13080 .001 .1683 .7850
WM Up to 5
year
6-10 year .10849 .08574 .416 -.0936 .3106
> 10 year -.38222* .08399 .000 -.5802 -.1842
6-10
year
Up to 5 year -.10849 .08574 .416 -.3106 .0936
> 10 year -.49071* .08574 .000 -.6928 -.2886
> 10
year
Up to 5 year .38222* .08399 .000 .1842 .5802
6-10 year .49071* .08574 .000 .2886 .6928
JS Up to 5
year
6-10 year -.02598 .06550 .917 -.1804 .1284
> 10 year -.45833* .06416 .000 -.6096 -.3071
6-10
year
Up to 5 year .02598 .06550 .917 -.1284 .1804
> 10 year -.43235* .06550 .000 -.5867 -.2780
> 10
year
Up to 5 year .45833* .06416 .000 .3071 .6096
6-10 year .43235* .06550 .000 .2780 .5867
EP Up to 5
year
6-10 year -.08150 .06252 .394 -.2289 .0659
> 10 year -.99136* .06124 .000 -
1.1357
-.8470
6-10
year
Up to 5 year .08150 .06252 .394 -.0659 .2289
> 10 year -.90986* .06252 .000 -
1.0572
-.7625
> 10
year
Up to 5 year .99136* .06124 .000 .8470 1.1357
6-10 year .90986* .06252 .000 .7625 1.0572
*. The mean difference is significant at the 0.05 level.
Interpretation
It is unwise to use multiple t-tests simultaneously as it decreases the confidence in results.
Hence Tukey’s test was applied which tells whether the significant difference is between up
to 5 year & 6-10 year or between 6-10 year & > 10 year or up to 5 year & > 10 year.
Tukey’s test here tells the difference is significant between up to 5 year and >10 year.
d. Experience
HA9. Experienced workers >5 years score higher than up to 5
87
Table 4.31 Group Statistics
Experience N Mean Std. Deviation Std. Error Mean
HM Up to 5 year 133 5.3850 .70394 .06104
> 5 year 130 5.9092 .57499 .05043
SM Up to 5 year 133 5.7218 .91215 .07909
> 5 year 130 6.2031 .78552 .06889
WM Up to 5 year 133 5.8075 .60523 .05248
> 5 year 130 6.2754 .49385 .04331
JS Up to 5 year 133 4.1836 .41752 .03620
> 5 year 130 4.5526 .46591 .04086
EP Up to 5 year 133 4.8287 .56317 .04883
> 5 year 130 5.5564 .40513 .03553
88
Table 4.31a Independent Samples t-Test
F Sig. T d.f. Sig. (2-tailed)
HM .740 .390 -6.606 261 .000
SM 1.204 .273 -4.580 261 .000
WM .842 .360 -6.860 261 .000
JS 10.615 .001 -6.767 261 .000
EP 7.820 .006 -12.005 261 .000
Interpretation
Levene's test for equal variances assumed (EVA) has been considered for all research
variables. We report the first line of t-test results. Table 4.37a gives the results of independent
samples t-test application on the mean differences between the experience <5 and >5 years of
respondents on all five research variables. The t-value and its associated level of significance
lead us to reject the null hypothesis of no difference. For H7substantiation, with p-value of
<0.0001, quiet less than the maximum acceptable error of 5% (0.05), the null hypothesis was
rejected at 0.05, so the difference between the mean scores between the experience of
respondents was statistically significant on all research variables.
89
4.6 Discussion
This chapter shows the overall summary and discussion of significant findings. The first part
is an overview of the study comprising of restatement of the objectives and research
questions (chapter 1), materials & methods (chapter 3) and results (chapter 4). The second
part discusses the most significant findings and integrating them with the existing theory. In
discussions the researcher positions his research findings through empirical evidence and
their comparison to the existing research on the topic. This study was aimed to achieve the
following objective:
4.6.1 Restatement of the objectives
1. To measure Correlations b/w EP with HM, SM, WM & JS
2. To compute Cause & Effect relationship b/w EP & HM, SM, WM & JS
3. To test the mediation of JS b/w EP & HM, SM, WM respectively
4. To compute demographic group mean differences of employees
The purpose of the study was to answer the following research questions:
1: Is there any statistically significant correlation between the EP and HM, SM, WM,
and JS respectively in sugar mills employees of KP?
2. Is there any statistically significant cause-n-effect relationship between the predictors
the EP (criterion) and HM, SM, WM, & JS (predictors)?
3. How far the relationship between EP (DV) and the HM, SM & WM (IVs)
respectively is mediated by the mediator?
4. Is there any role of Age, Residence, Education, and Experience (demographics) in
changing the responses of the employees about all five research variables; HM, SM,
WM, JS, and EP?
90
4.6.2 Materials and Methods
Survey approach was selected in which a representative sample from the total population was
selected to which the findings of the sample were generalized. The population comprised of
all the employees of all six functional sugar mills in KP, Pakistan having 3956 employees.
The sample size was 319 estimated on the statistics of the pilot study. Disproportionate
stratified random sampling was decided. Mill workers in northern & southern regions
constituted two strata. Northern region comprised of population of two working mills;
Khazana sugar mill, Peshawar and Premier sugar mill, Mardan having 1266 employees.
Southern region had four working sugar mills; Chashma-1 sugar mill, Chashma-2 sugar mill,
Al-Moiz sugar mill, Miran sugar mill having 2690 employees. Two mills were selected, one
mill each from each strata on the basis of simple random sampling technique. Permission
from management of Khazana Sugar Mill, Peshawar & Chashma Sugar Mill-1, D.I.Khan was
sought. Sampling frame for both the mills was formed, out of which the sample was selected
using simple random sampling technique. Sample comprised of 103 subjects from northern
and 216 from southern region (Table 3.2). All employees were eligible. Refusal to respond to
the questionnaire was the only exclusion criteria. Out of total 319 distributed questionnaires,
263 were received as usable for analysis. Our return rate was 82% which is acceptable.
HA1. EP is statistically significantly & positively correlated with HM, SM, WM & JS
Since, the data was normally distributed, thus for H1 testing, Pearson correlation test was
used. As the last two rows reveal that in table 5.1, EP and HM (r = .533, p<.001), EP and SM
(r = .365, p<.001), EP and WM (r = .477, p<.001) and EP and JS (r = .540, p<.001) which
indicates a moderately strong positive relationship: the higher the levels of HM, SM, WM
and JS, the higher the level of EP tends to be. The second line shows the probability that the
correlation occurred by chance, only in <1 out of 1000 samples of 263 respondents. Therefore
H1was accepted.
Table 4.32 Correlations Summary
**Correlation is significant at the 0.01 level (2-tailed).
HM SM WM JS
EP R .533 .365 .477 .540
P-value <.001 <.001 <.001 <.001
91
According to a study by Yusuf, Anis & Novita (2012), as the levels of Health (HM) and
Safety measures (SM) rise, EP rises and vice versa. According to studies conducted by
(Iheanacho & Ebitu, 2016) in Cement companies a significant relationship was found
between industrial health and employee’s performance and between industrial SM and
employee’s performance. The results are similar because the study has been conducted in
Nigeria; a developing country like Pakistan. In another study from Masqat, Oman significant
correlation was observed between lack of health and safety facilities and poor employee’s
performance (P <0.01), the reason for similarity of findings being once again due to both the
studies belonging to developing countries (Shikdar & Swaqed, 2003).
A study conducted by Sawe (2013) in a sugar company of Kenya found a positive significant
relationship between OH practices and employee’s performance. Womoh et al. (2013) says
occupational safety and occupational HM positively correlate with Employee Performance.
In a study by Lowe, Schellenberg & Shannon(2003), employees in healthier work
environments had significantly higher job satisfaction and higher performance. Kasturo et al.,
2010 says that occupational health and safety related problems negatively affect worker
output directly, resulting in high rate of injuries. According to Ashfaq (2011) and Qureshi et
al. (2013), the physical working conditions show a significant relationship with EP. There
was a significant correlation between the hospital safety and the Employee Performance in a
study by Mardani, Tabibi, & Riahi (2012).
According to Rubina et al. (2008) a negative relationship between job stress and job
performance concludes. Bashir & Ramay (2010), say there is a negative correlation between
job stress and performances. According to Dar et al. (2011) employees job performance is
related to job stress. In short, almost all the study results were in line with the results of
present study as far as association between HM, SM, WM, JS and EP is concerned.
HA2. EP is predicted by HM, SM, WM & JS
92
Table 4.33 Summary of the Predictions
Predictors Criterion EP
R2 40%
1 HM .000
2 SM .044
3 WM .318
4 JS .000
The table 4.23 shows that 0.400 is proportion of the variance R2 in EP accounted for by the
four independent variables. R2=0.400 means that 40 % of the variance in EP is accounted for
by the four independent variables while 60% is explained by other variables excluded from
this research. Therefore it is proved that independent variables contribute positively towards
change in the dependent variable. The level of significance is 0.000. R2 is actually the square
of correlation r (0.632). The p-values of predictors are far less than the alpha of 0.05 except
WM. The null hypothesis was rejected; therefore the H2 was accepted as true. Furthermore,
table 4.23b, presents the statistics on the role of predictors (HM, SM, WM and JS) in terms of
beta values. Every increase in HM of 1 unit SD is associated with an average increase in the
EP of .408 units SDs, controlling for the effect of the other IVs. If SM increases by one unit,
the EP will increase at sugar mills KP by .128 units respectively. WM gives non-significant
result with p-value greater than the required threshold of 0.05. If JS increases by one unit, the
EP will increase at sugar mills KP by .437 units. Hence becomes an excellent decision
making tool. For predictors measured in different units we use standardized beta coefficients
by standardizing variables i.e. mean=0 and SD=1. If R2 were equal to 1, all variance in EP
will be explainable by the predictors and regression model will fit the data well and vice
versa. The estimate is statistically significantly different from zero. This leads us to reject the
H0. There does appear a positive relationship between HM, SM and WM and EP across sugar
mills in KP, Pakistan. The regression equation to predict EP is therefore as follows:
EP=1.278+0.408HM+0.128SM+0.077WM+.437JS.
Naharuddin & Sadegi (2013) says physical environment has significant variance in the
dependent EP. According to a study by Yusuf, Anis & Novita (2012), occupational health
and safety was a significant predictor of EP. In a study by Viva & Dumondor (2017) safety
and health significantly affect Employee Performance. Physical working conditions tell the
positive significant effect on the EP (Ashfaq Ahmad, 2011). According to Sawe (2013), the
93
occupational health and safety practices in the model account for 81.2% variation in the
employee productivity in Sugar Company.
The study of the Keitany (2014) on the other hand established that welfare programs have
had positive impact on the Employee Performance at Kenya Pipeline Company.
HA3-5. JS strengthens the relationship b/w EP & HM, EP & SM & EP & WM
Table 4.34 Summary of Mediation Analysis
Model No. Model R1 2 Rm 2 β1 β2 βm
1 HM→JS→EP .284 .386 .469 .310 .468
2 SM→JS→EP .133 .327 .252 .140 .600
3 WM→JS→EP .227 .359 .486 .297 .521
R1 2 = Variance without mediation
Rm 2 = Variance with mediation
β1 = impact before mediation
β2 = impact after mediation
βm = Effect of mediator
Rm 2 due to the mediation of JS has increased and more than before mediator inclusion.
Similarly the beta-weights with the inclusion of the mediator have been affected. All
Independent variables show reduced (β2 column) beta weights, which were previously high
(β1 column) and another beta-weight (βM column) has been added. Thus, we see increased
overall impact of predictors on criterion due to mediator, but reduced individual impact of
predictors due to mediator.
HA3. JS strengthens the relationship b/w EP & HM
In a study conducted by Yusuf, Anis & Novita (2012), JS acting as mediator between Health
facilities and measures of OHS and EP gives significant results. In another study job
satisfaction partially mediates the relationship between management of health related
problems and issues and job performance (Hasanzade, 2013).
HA4. JS strengthens the relationship b/w EP & SM
94
According to the results of Khaqan, 2017 where job satisfaction mediates the relationship
between physical environment and performance. Job satisfaction partially mediated physical
environments to workers’ Performance (Fathi, 2015). Similarly Srivastava’s (2008) finding
that workers who perceived their physical work environment to be safe were more satisfied
with their jobs supported this finding. This finding supports our results. Job satisfaction has a
significant mediating role between the relationship of Physical Environment and performance
rate on a project (Akanni et al., 2015).
HA5. JS strengthens the relationship b/w EP & WM
Job satisfaction acted as mediator between occupational stress and EP (Nbirye, 2010). JS
holds a mediating effect between the relationship between worker welfare conditions and EP.
This suggests that those workers who perceive working conditions to be poor or bad are less
satisfied from their jobs and consequently are not performing satisfactory.JS partially
mediated the relationships psychological satisfaction of employees and EP (Olcer, 2015).
HA6-9. Demographics impact all 5 Research Variables
The demographics relate to the personal attributes of individuals. In industrial workplaces,
the workplace variables; HM, SM, WM and JS impacts on EP may vary with Age,
Residence, Education and Experience as reported by different researchers in the literature.
Employee feedback on these workplace characteristics could help in workplace design
determination.
Table 4.35 Summary of the Demographic Impacts on Research Variables
VARIABLES AGE RES EDU EXP
1 HM .000 .070 .000 .000
2 SM .000 .060 .000 .000
3 WM .000 .090 .000 .000
4 JS .000 .150 .000 .000
5 EP .000 .090 .000 .000
HA6. Older employees score higher than Youngers on 5 RVs
95
Table 5.4 gives the results of independent samples t-test application on the mean differences
between the Age 19-40 and 41-60 years respondents on all five research variables. For H6
substantiation, with p-value of <0.0001, the null hypothesis was rejected at 0.05, so the
difference between the mean scores between the age 19-4- and 41-60 years was statistically
significant on all research variables. According to our research, H6 is accepted.
Senior workers from 41-60 years of age feel better towards research variables than their
counterparts. Similar findings have been reported by other studies who found that younger
workers were more likely to suffer from occupational injury than their older counterparts
(Tadesse & Kumie, 2017). However Lowe, Schellenberg & Shannon, 2013 who states that
workers who are younger are more likely to perceive their work environment as healthy as
compared to workers between the ages of 25 and 54 and Bhattacherjee, 2012, who claims
younger workers show better enthusiasm and knowledge about OHS.
HA7. Urban employees score higher than rural
The null hypothesis was accepted at 0.05, so the difference between the mean scores between
the urban and rural respondents was statistically non-significant on all research variables.
Thus H7 stands rejected.
We found no study that compares the EP with regards to residence of workers, whereas in our
population, rural workers were having similar perceptions to urban workers towards research
variables.
HA8. Educated >10 years score higher than 6-10 years & up to 5 years
Table 4.36 reports F-ratio statistic. When b/w groups variation is more than within groups,
probability is high that IVs have resulted group differences. Assumptions of normality and
homoscedasticity were evaluated as a preliminary step and there were no serious violations.
All the research variables are giving significant result evident from F tests and with p-values
less than required alpha value of 0.05. Thus H8 stands accepted.
The findings are against the study by Lowe, Schellenberg, & Shannon (2013) who stated
differences by educational categories were not significant. The educated workers tend to be
96
more responsive in receiving instructions and doing new tasks and easily adopt new
technology which increases their ability to improve job performance (Kasika, 2015). Illiterate
workers are in majority especially in developing countries and use of PPEs for hazardous jobs
has always been a challenging task (Malik et al., 2010).
HA9. Experienced workers >5 years score higher than up to 5 years
Table 4.37a gives the results of five independent samples t-test applications on the mean
differences between up to 5 years and > 5years experience respondents on all five research
variables. All the tests show significant p-values indicating critical differences of attitudes
between the two groups of respondents based on experience. Therefore experience has impact
on all research variables, with p-values less than required alpha value of 0.05. Thus H9 stands
accepted. Experienced workers of > 5 years feel better towards research variables than their
counterparts.
More experienced workers had more positive perceptions regarding OHS, JS and EP (Ali &
Davies, 2003). Other researchers such as for example Avolio, Waldman, & McDaniel, 1990;
Tadesse & Kumie, 2007) say more experienced workers performed well than less
experienced ones.
97
Chapter 5: CONCLUSION AND RECOMMENDATIONS
This chapter presents the summary of results, conclusions, recommendations
for practice, policy implications, practical implications, and future research directions.
Conclusions are based on discussions whereas recommendations are conclusion-based. In the
conclusion we state the accomplishment of objectives explicitly. Valid theoretical
consideration of this research are given to sure its academics contribution explicitly. Theory
has two components; variables and their inter-connections. Coming down from multiple
theories governing our variables, we made our own model using all the existing relevant
models.
5.1 Summary of Results
The results summary is presented as follows:
a. Correlations Summary
Table 5.1 Correlations Summary
**. Correlation is significant at the 0.01 level (2-tailed).
For H1 testing, Pearson correlation test indicates a medium to strong positive relationship: the
higher the levels of HM, SM, WM and Job Satisfaction (JS), the higher the level of EP tends
to be. Therefore H1was accepted.
b. Predictions Summary
Table 5.2 Summary of the Predictions
Predictors Criterion EP
R2 40%
1 HM .000
2 SM .044
3 WM .318
4 JS .000
HM SM WM JS
EP R .533 .365 .477 .540
P-value <.001 <.001 <.001 <.001
98
Simple regression predicts that R2 = 0.400 indicating 40 % of the variance in EP is accounted
for by the four independent variables, therefore it is proved that independent variables
contribute positively towards change in the dependent variable. The level of significance is
<0.0001 (Table 4.23a). The p-values of predictors are far less than the alpha of 0.05 except
WM, leading us to reject the null hypothesis, therefore the H2 was accepted as true.
The estimated beta value between HM to EP of 0.408 tells that every increase in HM of 1
unit SD is associated with an average increase in the EP of .408 units SDs, controlling for the
effect of the other IVs. If safety measures (SM) increase by one unit, the EP will increase at
sugar mills KP by .128 units respectively. WM gives non- significant result with p-value
greater than the required threshold of 0.05. If JS increases by one unit, the EP will increase at
sugar mills KP by .437 units. Hence becomes an excellent decision making tool. The
regression equation to predict EP is therefore as follows:
EP=1.278+0.408HM+0.128SM+0.077WM+.437JS.
c. Mediations Summary
Table 5.3 Summary of Mediation Analysis
Model No. Model R1 2 Rm 2 β1 β2 βm
1 HM→JS→EP .284 .386 .469 .310 .468
2 SM→JS→EP .133 .32 .252 .140 .600
3 WM→JS→EP .227 .359 .486 .297 .521
R1 2 = Variance without mediation
Rm 2 = Variance with mediation
β1 = predictor’s impact before mediation
β2 = impact after mediation
βm = Effect of mediator
Rm 2 due to the mediation of JS has increased and more than before mediator inclusion.
Similarly the beta-weights with the inclusion of the mediator have been affected. All
Independent variables show reduced (β2 column) beta weights, which were previously high
(β1 column) and another beta-weight (βM column) has been added. Thus, we see increased
overall impact of predictors on criterion due to mediator, but reduced individual impact of
predictors due to mediator.
99
d. Tests of Significance Summary
Table 5.4 Summary of the Demographic Impacts on Research Variables
AGE RES EDU EXP
1 HM .042 .070 .000 .041
2 SM .019 .060 .003 .000
3 WM .000 .090 .022 .058
4 JS .010 .150 .030 .000
5 EP .000 .090 .040 .000
HA6. Older employees score higher than Youngers on 5 RVs (t-test)
HA7. Urban employees score higher than rural (t-test)
HA8. Educated >10 years score higher than 6-10 years & up to 5 years (ANOVA)
HA9. Experienced workers >5 years score higher than up to 5 (t-test)
The difference between the mean scores between the age 19-4- and 41-60 years was
statistically significant on all research variables. According to our research, H6 is accepted.
Old workers from 41-60 years of age feel better towards research variables than their
counterparts.
The difference between the mean scores between the urban and rural respondents was
statistically significant on all research variables. Thus H7 stands accepted.
All the research variables are giving significant result evident from F tests and with p-values
less than required alpha value of 0.05. Thus H8 stands accepted.
Between up to 5 years and > 5years experience respondents on all five research variables, all
the tests show significant p-values indicating critical differences of attitudes between the two
groups of respondents based on experience. Therefore experience has impact on all research
variables, with p-values less than required alpha value of 0.05. Thus H9 stands accepted.
Experienced workers of > 5 years feel better towards research variables than their
counterparts.
100
Table 5.5 Summary of Statistical Tests and Hypotheses Testing Results
S.
No
Hypotheses Tests Results
H1 The predictors (HM, SM, WM and JS) on one hand are
associated with the criterion variable (EP) on the other
hand respectively in sugar mills workers of KP province
of Pakistan.
Correlation Accepted
H2 The criterion EP significantly explains the variance by
the four predictors; HM, SM, WM and JS.
Multiple
Regression
Accepted
H3 The mediator JS significantly partially mediates the
relationship between predictor HM and outcome EP.
Mediation Accepted
H4 JS significantly partially mediates the relationship
between SM (IV) and EP (DV).
Mediation Accepted
H5 JS significantly partially mediates the relationship
between WM (X) and EP (Y).
Mediation Accepted
H6 There are significant demographic group mean
differences of age on all research variables
t-test Accepted
H7 There are significant demographic group mean
differences of residence on all research variables
t-test Rejected
H8 There are significant demographic group mean
differences of education on all research variables
ANOVA Accepted
H9
There are significant demographic group mean
differences of experience on all research variables
t-test Accepted
101
5.2 Conclusion
Conclusion based on the results of the hypotheses shows that five research variables and four
demographics generate multiple decision points out of which some are very much crucial.
Table 5.1 shows hypotheses testing results about the diverse relationships/ differences
between different variables operating in the theoretical framework of this study which are
logically supported by the existing and current research.
Coming down from multiple theories governing our variables including ‘The Maslow’s
theory of hierarchical of needs’ and Social exchange theory, we made our own model as
academic contribution. We accomplished our objectives explicitly as follow.
1. EP is significantly & positively correlated with HM, SM, WM & JS
2. EP is being explained by HM, SM, WM & JS except for WM
3. EP-HM, EP-SM & EP-WM relationships are supported by JS
4. Demographic group mean differences are there except for Residence
The high positive significant correlational relationship between EP on one hand and all other
research variables on the other can lead us to deduce that if EP is to be increased, the
employees and their families need to be provided a range of health, safety, welfare measures
& JS to improve their health status and quality of life.
All the predictors, especially those having higher beta values such as HM and JS need to be
focused to improve performance. By ignoring the HM, SM, WM and JS, the organization is
perhaps not harnessing the full potential and talent of workers. Effectiveness of the sugar
mills will increase if the abilities of the workers are fully utilized. This is the unique thing
about sugar mill workers rather than other factory workers. The insight or new knowledge of
the theory are the R2 values of the multiple regression model of this study, the R2 change in
mediation analysis and the demographic impacts of the sugar mill workers in their
perceptions about OHS, JS & EP. As far as the insight or new knowledge is concerned, our
claim is that before this investigation, we were unaware of the predictors of EP in KP, which
was a knowledge gap for us. After conducting this survey, the evidence based
recommendations can be given for our specified population i.e. sugar mill workers of KP.
This is the unique thing about sugar mill workers as compared to other factory workers.
Sugar mills employees want to put their maximum as far as employee performance is
102
concerned. But in return they expect job satisfaction through health promotion, disease
prevention, safety from hazards and respectable standard of living. That is the insight of the
theory behind this investigation.
Full mediation would mean OHS improvement has zero roles and only JS needs to be
focused, whereas partial mediation would mean if JS is focused besides OHS, the poor
performance resulting from inappropriate OHS can be addressed. Here in this research, the
role of mediation was found partial in all the three Models, showing that JS role can never be
ignored while considering OHS interventions to improve EP. An employee needs to be
mentally relaxed from job. Work in comfortable setting matters only if the employee is
satisfied from his job.
As for as demographics are concerned, older workers show positive perceptions regarding all
research variables than their younger counterparts. Same is the case with more educated and
more experienced ones. Younger, less educated and less experienced workers score low on
the research variables.
This is the most dominant theme that the views of workers between different demographic
groups vary regarding performance of employees along with its predictors in sugar mills of
KP showing context of the problem is very powerful. The highly educated and more
experienced employees can understand the situation better as they possess rationality and
thinking power. Workers’ ability to understand and use advanced technology is determined
by the level of their education. Illiterate workers are always difficult to convince regarding
use of PPEs and modern technology.
5.3 Recommendations for practice
1. HRM should ensure optimal/ accepted standards of HM & SM. This will improve EP
directly and through better JS by tapping & utilizing full potential of employees. HRM
through leadership & communication skill roles should ensure continued capacity building of
the employees on OHS especially young, illiterate & inexperienced.
2. Ministry of Labor should ensure requisite OHS legislation, regular inspections and
implementation of OHS regulations.
103
5.4 Policy Implications
This investigation provides knowledge of dynamics of work environment. They know the
relative importance of different variables operative in improving the daily lives of the
workers. This ultimately affects the performance and organizational goals. Government
legislating machinery may be guided in the right direction. Labor department will be able to
plan and execute their policies accordingly. Public health physician will better educate
employees and supervisors on matters relating to work hazards and how to best protect their
workforce from them. This study will generate new knowledge and serve as spadework for
the forthcoming researchers to fill the gaps between the standards and real practices.
Challenges in health and safety implementation in industrial work environment as
recommended may be overcome. The knowledge of the demographics of workers and their
effect on EP may be a special area of interest for all the managers. The role of care providers,
safety engineer and human resource manager is extremely crucial in this regard. The study is
of importance to the sugar mill employees who will get awareness about different workplace
hazards and multiple issues of occupational health.
5.5 Practical Implications
The results of this study can guide the managers and other practitioners in the right direction
to bring changes in the work environment so as to make the lives of their workforce easier &
safer. The ultimate job satisfaction the workers would get would help achieve the individual
goals of the workers as well as the organizational goals set for the managers. The managers
could arm themselves to know which variable to asses and why while conducting their own
surveys. This study will enable the sugar mills management to make informed decisions
about various human resource management practices and ensuring good and safe working
conditions for employees.
Creation and implementation of a policy that sensitizes and allows for provision of
occupational health practices and safety management at workplace has a direct significant
effect on employee performance (Zhou, et al., 2015). OHS management is not the only aspect
for expecting better employee performance. Organizations should also develop strategies for
promoting job satisfaction through fulfilling employee needs, security to employees and
104
satisfaction to employees. The organization should strive to ensure that remuneration
packages are fair and equitable and measurably linked to performance (Lee, et al., 2016).
Our findings enable us to confirm the previous understanding in the field. The explanation
comes from the empirical data which is consistent with the understanding based on existing
literature. As far as the theoretical and additional contributions of this investigation are
concerned, it can be stated with clarity that performance of an employee is the result of his
satisfaction from his job, which may come as a result of conducive work conditions and good
standard of life for their families. Whatever improvements in the work conditions introduced
are not going to translate into improved EP, unless and until the employee is satisfied.
5.6 Future Research Directions
In academic research, common practice of researchers is select a topic and support it with
relevant literature, extract TFW and go for data collection accordingly. This is followed by
data analysis and conclusion after processing data as per established standards. Thematic
analysis is done for qualitative data and statistical analysis for quantitative data. The purpose
is to verify model on ground to identify knowledge gaps in the form of recommendations for
those responsible. It is the job of future researcher to find out whether the knowledge gap has
reduced or not as a result of interventions made by the high-ups.
Although the results of this study provide more thorough understanding of the EP of sugar mills
in KP and underlying factors that influence the job satisfaction, further empirical research with
more and new variables needs to be conducted to get a more complete picture. Research from
other industries, all over the country can be carried out to validate results of this study. Research
context was limited to sugar mill sector of KP, while the future research can be extended to
other industrial sectors of KP or all over Pakistan, especially with a larger sample size and
additional variables in the TFW. Demographics as predictors and moderation analyses by
using different combinations of variables can be tested. The reader is informed to embark on
analytical observational as well as interventional study design with rigorous generalization to
add to the knowledge on the topic. The theoretical model of this research may guide the
future researchers as spadework.
105
Chapter 6: REFERENCES
1. Awais, M., Malik, M. S., & Qaisar, A. (2015). A Review: The Job Satisfaction Act as
Mediator between Spiritual Intelligence and Organizational Commitment.
International Review of Management and Marketing, 5(4), 203-210.
2. Ajala, E. M. (2012). The influence of workplace environment on workers’ welfare,
performance and productivity. University of Ibadan. The African Symposium, 12(1),
342-323.
3. Ali, H & Davies, D. R. (2003). The effects of age, sex and tenure on the job
performance of rubber tappers. Journal of Organizational Psychology,76(3), 381-391
4. Al-Otaibi, F. M. S., Alharbi, M. F., & Almeleehan, A. (2015). Effect of total quality
management practices factors on the competitiveness: evidence from Saudi Arabia.
International Journal of Business and Management; 10(5), 85-97.
5. Amar, A. D., & Mullaney, K. (2017). Employee ability to innovate: how can
organizations recognize it. Procedia Computer Science, 122, 494-501.
6. Amponsah-Tawiah K., Ntow, M. A. O., & Mensah, J. (2015). Occupational health
and safety management and turnover intention in the Ghanaian mining sector. Safety
and Health at Work, doi: 10.1016/j.shaw.
7. Anitha, J. (2014). Determinants of employee engagement and their impact on
employee performance. International Journal of Productivity and Performance
Management, 63(3), 308-323.
8. Avolio, B. J., Waldman, D. A. & McDaniel, M. A. (1990). Age and work
performance in non-managerial jobs: the effects of experience and occupational type.
The Academy of Management Journal, 33(2), 407-422.
9. Babar, B.B., & Zaid, A.R.S. (2015). Evaluation of quality and technology gap in KPK
sugar industries of Pakistan. Institute of quality & technology management,
University of the Punjab. 1-26.
10. Babbie, E. (1995). The practice of social research. Belmont: Wadswonh publishing
company.
106
11. Babin, J., Boles, J. (1998). Employee behavior in a service environment: a model and
test of potential differences between men and women. J. Mark, 62,77- 91.
12. Hogstedt, C., & Pieris, B. (2000). Occupational safety and health in developing
countries. Review of strategies, case studies and a bibliography. Arbetslivsrapport
nr:17 issn 1400-8211 http://www.niwl.se/arb/
13. Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in
social psychological research: conceptual, strategic and statistical considerations.
Journal of Personality and Social Psychology, 51, 1173-1182.
14. Baroudi, J. J., & Orlikowski, W. J. (1988). A short-term measure of user information
satisfaction: A psychometric evaluation and notes on use. Journal of Management
Information Systems, 4(4), 44-59.
15. Bartlett, J. E., Kotrlik, J. W., & Higgins, C. C. (2001). Organizational research:
Determining appropriate sample size in survey research. Information Technology,
Learning, and Performance Journal, 19(1), 43-50.
16. Bashir, U., & Ramay, M. I. (2010). Impact of stress on employees job performance: a
study on banking sector of Pakistan. International Journal of Marketing Studies,
2(1,2),122-126.
17. Beri, G. C. (2008). Marketing research. New Delhi: Tata McGraw Hill.
18. Bhattacherjee, A. (2012). Social science research: principles, methods, and
practices.textbooks collection.Book.
19. Booth-Kewley, S., Edwards, J., Rosenfeld, P., & Thomas, M. (1997). How to conduct
organizational surveys: A step-by-step guide. Sage Publications.
20. Cameron, K. S. (1981). Construct space subjectivity problems in organizational
effectiveness. Public Productivity Review, 5(2), 105-121.
21. Cameron, K. S., & Freeman, S. L. (1991). Cultural congruence, strength, and type:
Relationships to effectiveness. Research in Organizational Change and Development,
5, 23-58.
22. Comrey, A. L. (1973). A first course in factor analysis. New York: Academic Press;
1973.
107
23. Cooper, D. R., Schindler P. S. (2001). Business Research Methods. New York:
McGraw-Hill.
24. Dar, L., Akmal, A., Naseem, M. A., & Khan, K. U. D. (2011). Impact of stress on
employees job performance in business sector of Pakistan. Global Journal of
Management and Business Research,11(6)
25. Siu, O., Phillips, D. R., & Leung, T. (2004). Safety climate and safety performance
among construction workers in Hong Kong: The role of psychological strains as
mediators. Accident Analysis & Prevention, 36(3), 359-366. doi: 10.1016/S0001-
4575(03)00016-2
26. Fathi, A. N. S. (2015). The mediating effect of job satisfaction on work environment
and productivity of the public petrochemical companies in Libya. Thesis submitted to
Othman Yeop Abdullah graduate school of business, Universiti Utara Malaysia, in
fulfillment of the requirement for the degree of doctor of philosophy).
27. Field, A. (2005). Discovering statistics using SPSS. Sage Publication Inc. Fisher,
2003).
28. Kubeck, J. E., Delp, N. D., Haslett, T. K., & McDaniel, M. A. (1996). Does job-
related training performance decline with age? Psychology and Aging, 11(1), 92-107.
http://dx.doi.org/10.1037/0882-7974.11.1.92.
29. Gyensare, M. A., Anku-Tsede, O., & Kumedzro, L. E. (2018). Occupational health
and safety dimensions and work outcomes in the mental hospitals in Ghana: the
moderating effect of job satisfaction. Advances in Safety Management and Human
Factors, 564-577.
30. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data
analysis. Seventh edition. Prentice Hall, Upper Saddle River, New Jersey.
31. Hassan, S. (2016). Impact of HRM practices on employee’s performance.
International Journal of Academic Research in Accounting, Finance and
Management Sciences, 6(1):15-22.
32. Hoboubi, N., Choobineh, A., Ghanavati, K. F., Keshavarzi, S., & Hosseini, A. (2017).
The impact of job stress and job satisfaction on workforce productivity in an Iranian
petrochemical industry. Safety and Health at Work, 8(1), 67-71. doi:
108
https://doi.org/10.1016/j.shaw.2016.07.002. http://dx.doi.org/10.1037/0882-
7974.3.3.307
33. Husebø, S. E., & Olsen, Ø. E. (2016). Impact of clinical leadership in teams’ course
on quality, efficiency, responsiveness and trust in the emergency department: study
protocol of a trailing research study. BMJ Open, 6(8), e011899.
http://doi.org/10.1136/bmjopen-2016-011899.
34. Hussain, A. B. (2011). The relationship between job satisfaction and job performance
among employees in trade winds group of companies. A master’s project for the
degree of master of human resource management centre, Open university, Malaysia.
35. Iheanacho, M. U., & Ebitu, E. T. (2016). Effects of industrial safety and health on
employees’ job performance in selected cement companies in cross river state,
Nigeria. International Journal of Business and Management Review, 4(3), 49-56.
36. Iqbal M. (2013). Mediational effect of stress coping strategies between workplace
stress and organizational performance: A case study of air traffic controllers of
Pakistan. European Journal of Management Sciences, 1(1), 42-56.
37. Khan, I., Nawaz, A., Qureshi, Q. A., Khan, Z. A., Khan, M. S., & Ali, W. (2016) The
Impacts of Job Burnout and Absenteeism on Employees Performance in
HEIs. Information and Knowledge Management, 6(1):1-4. ISSN 2224-5758 (Paper)
ISSN 2224-896X (Online). At: http://www.iiste.org
38. Jilcha, K., & Kitaw, D. (2016). A literature review on global occupational safety and
health practice & accidents severity. International Journal for Quality Research,
10(2), 279-310.
39. Lee, K., Kwon, H., Cho, S., Kim, J., & Moon, I. (2016). Improvements of safety
management system in Korean chemical industry after a large chemical accident.
Journal of Loss Prevention in the Process Industries,42, 6-13.
40. Savino, M. M., & Shafiq, M. (2018). An extensive study to assess the sustainability
drivers of production performances using a resource-based view and contingency
analysis. Journal of Cleaner Production, 204, 744-752.
41. Kadam, A. R., Waghole, S. N., & More, S. (2012). An empirical study on welfare
facilities and its impact on employee’s performance at Hindalco, Belgaum. Indian
Journal of Management Science (Ijms), 2(1). www.scholarshub.net/ijms/
109
42. Kamkari, K., Ghafourian, H., & Ghadami, M. H. (2014). Impact welfare services and
benefits on the performance of staff of the inspector general organization. Journal of
Applied Environmental and Biological Sciences, 4(3), 114-119.
43. Kasika, B. D. (2015). The effect of educational qualification on job performance: the
case of social security commission in Namibia (SSC). A mini-thesis submitted in
partial fulfillment of the requirements for the degree of master of business
administration of the University of Namibia.
44. P. Katsuro, C. T. Gadzirayi, Taruwona, M., & Mupararano, S. (2010). Impact of
occupational health and safety on worker productivity: a case of Zimbabwe food
industry. African Journal of Business Management, 4(13), 2644-2651. Available
online at http://www.academicjournals.org/AJBM ISSN 1993-8233 ©2010 Academic
Journals.
45. Keitany, B. J. (2014). Perceived relationship between employee welfare programs and
employee performance at Kenya Pipeline Company. A Research Project Submitted In
Partial Fulfillment Of The Requirement For The Award Of The Degree Of Master Of
Business Administration, School Of Business, University Of Nairobi 2014
46. S. Kanchana, P. Sivaprakash, Sebastian Joseph. (2015). Studies on labour
safety in construction sites. The Scientific World Journal, Article ID 590810, 6
pages. https://doi.org/10.1155/2015/590810.
47. Khan, A. W., Moshammer, H. M., & Kundi, M. (2015). Industrial hygiene,
occupational safety and respiratory symptoms in the Pakistani cotton industry. BMJ
Open, 5:e007266. Doi: 10.1136/bmjopen-2014- 007266.
48. Khaqan, M. (2017). Impact of work environment on project success : mediating role
of job satisfaction and moderating effects of demographics. Department of
management sciences Capital University of science & technology, Islamabad.
[Thesis].
49. Kim, K. M., & Oh, H. (2015). Clinical experiences as related to standard precautions
compliance among nursing students: a focus group interview based on the theory of
planned behavior. Asian Nursing Research, 9(2), 109-114.
110
50. Shaukat, M., Chaudry, D. I. A., Rasheed, F., Kamal, B., & Ashraf, M. A. (2018).
Assessment of Health and Safety Issues in a Manufacturing Industry. Journal of
Information System and Technology Management, 3 (10), 90-100.
51. Koh, Y., Hegney, D., & Drury, V. (2011). Comprehensive systematic review of
healthcare workers' perceptions of risk and use of coping strategies towards emerging
respiratory infectious diseases. Int J Evid Based Health, 9(4), 403-19. doi:
10.1111/j.1744-1609.2011.00242.x.
52. Qureshi, M. I., Bashir, S., Saleem, A., Javed, A., Saadat, U. R., & Safdar, M. Z.
(2013). Analysis of various determinants which affect on job performance: A case
study on private and public universities employees of D.I.Khan. Gomal University
Journal of Research, 29(1), 62-70.
53. Kumari, M. S. & Tatareddy, M. (2014). Impact of employee welfare facilities on job
satisfaction: a study with reference to Secunderabad division of south central railway.
International Journal of Economic and Business review,1(12),189-197.
54. Hasnain, M., Khan, A., Shah, S., Naeem, M. M., & Raza, M. (2018). Construction
health and safety conditions and climate in Pakistan. J mech cont & math Sci., 13, (5),
60-72.
55. Anwar, Z., Mustafa, A., Alib, M. (2019). Appraisal of process safety management
practices in refining sector of Pakistan. Process Safety and Environmental
Protection.128, 36-40.
56. Leite, N. R. P., Rodrigues, A. C. A., & Albuquerque, L. G. (2014). Organizational
commitment and job satisfaction: what are the potential relationships? Brazilian
Administration Review - BAR, 11(4), 476–495. https://doi.org/10.1590/1807-
7692bar2014276.
57. Locke, E. A. (1976). The nature and causes of job satisfaction. in m. d. dunnette (ed.),
Handbook of Industrial and Organizational Psychology. Chicago: Rand McNally.
58. Lowe, G. S., Schellenberg, G., & Shannon, H.S. (2003). Correlates of employees'
perceptions of a healthy work environment. Am J Health Promot, 17(6), 390-399.
59. Anubhai, P. (1989). Sickness in Indian Textile Industry. Economic and Political
Weekly, 24 (27) 1550-1552.
111
60. Rajaprasad SVS. (2018) Prediction of fatal accidents in indian factories based on
ARIMA. Production engineering archive,. 18, 24-30.
61. Malik, M. I., Ahmad, A, Gomez, S. F., & Ali, M. (2011). A study of work
environment and employees’ performance in Pakistan. Available online at
http://www.academicjournals.org/AJBM. DOI: 10.5897/AJBM11.1502 ISSN 1993-
8233 ©2011 Academic Journals.
62. Malik, N. (2010). Perspective of occupational health and safety in textile industry.
(PhD thesis submitted to University of Agriculture, Faisalabad, Pakistan).
63. Malik, N., Maan, A. A., Pasha, T. S., Akhtar, S., & Ali, T. (2010). Role of hazard
control measures in occupational health and safety in the textile industry of Pakistan.
Pak. J. Agri. Sci., 47(1), 72-76.
64. Manandhar, S. (2015). Impact of welfare facilities on the performance of employees
in NGOs in Nairobi. A case of Kenya red cross Nairobi branch. A research project
report submitted in partial fulfillment of the requirement for the award of a Master of
Arts degree in project planning and management of the University of Nairobi.
65. Mardani, S., Tabibi, S. J., & Riahi, L. (2012). Relationship between safety and staff
performance in hospital. International Journal of Hospital Research, 2(4), 205-214.
66. Jankingthong, K., & Rurkkhum, S. (2012). Factors affecting job performance: A
review of literature. Silpakorn university journal of social sciences, humanities, and
arts, 12 (2), 115-127.
67. Sheikh, M. F., Shah, S. Z. A., & Akbar, S. (2018). Firm performance, corporate
governance and executive compensation in Pakistan. Applied
Economics, 50,18, 2012-2027, DOI: 10.1080/00036846.2017.1386277.
68. Jaiswal, P., Gadpayle, A. K., Singhal, A. K., Sachdeva, S., Modi, R. K., Padaria, R.,
& Ravi, V. (2015). Job satisfaction among hospital staff working in a Government
teaching hospital of India. Medical Journal of Dr. DY Patil Vidyapeeth, 8,131-7.
69. Mirzoev, T., & Kane, S. (2017). What is health systems responsiveness? review of
existing knowledge and proposed conceptual framework. BMJ Global Health, 2(4),
e000486. http://doi.org/10.1136/bmjgh-2017-000486.
112
70. Mousa, K. M., Fouad, N. A., & Bader el-dein, S. A. (2014). Assessment of knowledge
and self-reported practices of iron mines’ workers about pneumoconiosis in baharia
oasis, Giza Governorate. Med. J. Cairo Univ., 82(2), 23-29.
71. Munir, A, Ashraf, M. A., Nasir, A., Hensel, O., & Iqbal, M. (2012). Ergonomics and
occupational health in sugar industry of Pakistan. Pakistan Journal of Life and Social
Sciences, 10(1), 74-79.
72. Munisamy, S. (2013). Identifying factors that influence job performance amongst
employees in oil palm plantation, Malaysia. (Thesis).
73. Naharuddin, N. M., & Sadegi, M. (2013). Factors of workplace environment that
affect employees performance: a case study of miyazu Malaysia. International
Journal of Independent Research and Studies, 2(2), 66-78.
74. Nassazi, A. (2013). Effects of training on employee performance. Evidence from
Uganda. Business economics and tourism, 1-59.
75. Nawaz, A., Miankheil, A. K., Khan, S., & Kundi, G. M. (2012). Predicting the
university teachers’ performance: a survey of Gomal University (a PSO), DIK. Gomal
University Journal of Research, 28(2).
76. Nawaz, M., Khan, S., Chattha, M. U., Khan, & M., Bilal, M. (2015). Interactive
effect of sowing techniques and weed crop competition periods on the performance of
spring planted sugarcane (Saccharum officinarum L.). International Journal of
Modern Agriculture,4(3) Copyright © Zohdi Publisher ISSN: 2305‐7246
77. Nbirye, R. C. (2010). Occupational stress, job satisfaction, and job performance
among hospital nurses in Kampala Uganda. A dissertation.
78. Shah, S. M. M., Ali, R., Dahri, A. S., Brohi, N. A., Maher, Z. A. & Hameed, W.U.,
(2018). Determinants of Job Satisfaction among Nurses: Evidence from South Asian
Perspective. International Journal of Academic Research in Business and Social
Sciences, 8(5), 19–26.
79. Ohuruzor, C. L. O., Adebanjo, A., & Omoniyi, I. S. (2014). Occupational health and
safety. National open university of Nigeria. School of science and technology. Course
code: EHS 403.
113
80. Olcer, F. (2015). Mediating effect of job satisfaction in the relationship between
psychological empowerment and job performance. Theoretical and Applied
Economics. 22(3), 111-136.
81. Lee, H., & Cummings, G. G. (2008). Factors influencing job satisfaction of front line
nurse managers: a systematic Review. Journal of nursing management 16, 768–783.
82. Rageb, M. A., Abd-El-Salam, E. M., El-Samadicy, A., & Farid, S. (2013).
Organizational commitment, job satisfaction and job performance as a mediator
between role stressors and turnover intentions a study from an egyptian cultural
perspective. The Business & Management Review, 3(2), 51-73.
https://doi.org/10.1108/02683940210428074.
83. Ramamoorthy, R., Thooyamani, K. P., & Karthick, K. (2017) A study on
effectiveness of welfare measure and employee morale in tv sundaram iyengar & sons
limited, Madurai. International Journal of Pure and Applied Mathematics, 116(18),
269-273.
84. Ramya, T. J., Arepallli, B. S., & Lakshmi, P. (2016). A study on employee welfare
facilities and its impact on employee satisfaction at hotel industry with special
reference to Mysuru district. International Journal of Engineering Science and
Computing, 6(12), 3680-3686.
85. Ataro, Z., Geremew, A., & Urgessa, F. (2018). Occupational health risk of working in
garages: comparative study on blood pressure and hematological parameters between
garage workers and Haramaya University community, Harar, eastern Ethiopia. Risk
Manag Healthc Policy, 11, 35–44. Published online 2018 Mar
13. doi: 10.2147/RMHP.S154611
86. Bakhsh, K., Ahmad, N., Tabasum, S., Hassan, S., & Hassan, I. (2017). Health hazards
and adoption of personal protective equipment during cotton harvesting in Pakistan.
Science of The Total Environment, 598, 1058-1064.
87. Islam, M. S., Razwanul, I., & Mahmud, M. T. (2017). Safety Practices and Causes of
Fatality in Building Construction Projects: A Case Study for Bangladesh. Jordan
Journal of Civil Engineering, 11(2) 267-278.
88. Kazmi Saeed, R., Mussawar, S., Lodhi, R. N., Iqbal, A., Nayab, H. H., & Yaseen, S.
(2013). Factors affecting the performance of employees at work place in the banking
114
sector of Pakistan. Middle-East Journal of Scientific Research, 17(9), 1200-1208.
ISSN 1990-9233 © IDOSI Publications, 2013 DOI:
10.5829/idosi.mejsr.2013.17.09.12256
89. Salman, S., Mahmood, A., Aftab, F., & Mahmood, A. (2016). Impact of safety health
environment on employee retention in pharmaceutical industry: mediating role of job
satisfaction and motivation. Journal of Business Studies, 12(1), 185-197.
90. Sattar, A., & Shadiullah. (2011). The job satisfaction of district officers in Khyber
Pakhtoon Khwa Pakistan”, Thesis Gomal University.
91. Sawe, M. J. (2013). Effects of occupational health and safety practices on employees’
productivity in mumias sugar company, Kenya. A research thesis submited in partial
fulfilment for the award of master of science in human resource development MOI
University.
92. Prentice, R., Neve, G. D., Mezzadri, A., & Ruwanpura, K. N. (2018). Health and
safety in garment workers’ lives: Setting a new research agenda. Geoforum 88, 157-
160.
93. Christos A. Damalas, & Spyridon D. Koutroubas. (2018). Farmers' behaviour in
pesticide use: A key concept for improving environmental safety. Current Opinion in
Environmental Science & Health. 4, 27-30.
94. Sekaran, U. (2003). Research methods for business: A skill-building approach. New
York: Johan Wiley & Sons.
95. Sekaran, U. (2006). Organizational behavior: Text and cases. New York: McGraw-
Hill.
96. Sembe, F., & Ayuo, A. (2017). Effect of selected occupational health and safety
management practices on job satisfaction of employees in university campuses in
nakuru town, Kenya. Journal of Human Resource Management, 5(5), 70-77. doi:
10.11648/j.jhrm.20170505.11.
97. Sethibe, T. & Steyn, R. (2016). Innovation and organisational performance: A critical
review of the instruments used to measure organisational performance, Southern
African Journal of Entrepreneurship and Small Business Management,
8(1),a50.http://dx.doi. org/10.4102/sajesbm.v8i1.50.
115
98. Shaffril, H. A. M., & Uli, J. (2010). The influence of socio-demographic factors on
work performance among employees of government agriculture agencies in Malaysia.
The Journal of International Social Research, 3(10), 459-469.
99. H. Shahmina (2014). Impact of job satisfaction on professional commitment in higher
education. Galaxy International Interdisciplinary Research Journal, GIIRJ, 2(2), 1-
11.
100. Van Oldenborgh, G. J., Philip, S., Kew, S., Van Weele, M., Uhe, P., Otto, F. E. L et al
(2018). Extreme heat in India and anthropogenic climate change. Natural Hazards
and Earth System Sciences, 18, 365–381.
101. Shikdar, A. A., & Sawaqed, N. M. (2003). Worker productivity and occupational
health and safety issues in selected industries. Computers & Industrial Engineering,
45, 563-572.
102. Dhananjayan, V., & Ravichandran, B. (2018). Occupational health risk of farmers
exposed to pesticides in agricultural activities. Current Opinion in Environmental
Science & Health, 4, 31-37.
103. Wang, S., & Yi, X. (2011). It’s happiness that counts: full mediating effect of job
satisfaction on the linkage from lmx to turnover intention in Chinese companies.
journal of leadership studies, 6 (3), 332-356.
104. Ahmed, I., Usman, A., Nazir, M.S., & Shaukat, M. Z. (2018). Safety practices in
informal industrial segment of Pakistan. Safety Science, 110, 83-91.
105. Suárez, M., Asenjo, M., & Sánchez, M. (2017). Job satisfaction among emergency
department staff. Australasian Emergency Nursing Journal, 20(1), 31-36. doi:
https://doi.org/10.1016/j.aenj.2016.09.003.
106. Suresh, S., Kodikal, R., & Kar, S. (2015). Measuring job satisfaction and impact of
demographic characteristics among doctors of teaching hospitals. International
Multispecialty Journal of Health (IMJH), 1(9), 29-38.
107. Tabachnick, B. G., & Fidell, L. S. (2007). Using Multivariate Statistics. New York:
Harper Collins.
108. Tadesse, T., & Kumie, A. (2017). Prevalence and factors affecting work-related injury
among workers engaged in small and medium-scale Industries in Gondar Tezeral.
Journal of Occupational Medicine and Toxicology, 12, 25-34.
116
109. Singh, M., Singh, Thind, P. S., & John, S. (2018). Health risk assessment of the
workers exposed to the heavy metals in e-waste recycling sites of Chandigarh and
Ludhiana, Punjab, India. Chemosphere, 203, 426-433.
110. Unutmaz, S. (2013). Factors affecting job satisfaction of employees in a public
institution. A thesis submitted to the graduate school of natural and applied sciences
of Middle East technical university.
111. Inuwa, M. (2016). Job Satisfaction and Employee Performance: An Empirical
Approach. The Millennium University Journal, 1, (1); 89-100.
112. Veltri, A., Pagell, M., Behm, M., & Das, A. (2007). A data-based evaluation of the
relationship between occupational safety and operating performance. The Journal of
SH&E Research, 4(1), 1-22.
113. Dhananjayan, V., & Ravichandran, B. (2018). Occupational health risk of farmers
exposed to pesticides in agricultural activities. Current Opinion in Environmental
Science & Health. 4: 31-37.
114. Viva, R., & Dumondor, N. (2017). The influence of safety and health, and office
facilities on employee performance (case study of Pt Meares Soputan Mining). The
Influence of Safety and Health, 5(2), 256-266 ISSN 2303-1174.
115. Waititu, F., Kihara, P. & Senaji, T. (2017). Effect of employee welfare programmes
on employee performance: A case study of Kenya railways corporation. International
Academic Journal of Human Resource and Business Administration, 2(3), 611-631.
116. Wang, H., & Tao, L. (2012). Current situations and challenges of occupational disease
prevention and control in China. Industrial Health, 50, 73-9.
117. WHO Global plan of action on workers’ health (2008-2017): baseline for
implementation. Global country survey 2008/2009. Executive summary and survey
findings Geneva, April 2013.
118. Dwomoh, G., Owusu, E. E., & Addo, M. (2013). Impact of occupational health and
safety policies on employees’ performance in the Ghana’s timber industry: Evidence
from Lumber and Logs Limited. International Journal of Education and Research,
1(12), 1-14.
117
119. Ahmed, I., & Shaukat, M. Z. (2018). Computer users’ ergonomics and quality of
life –evidence from a developing country. International Journal of Injury Control and
Safety Promotion, 25:2, 154-161, DOI: 10.1080/17457300.2017.1415361
120. Yusuf, R. M., Anis, E., & Novita, S. (2012). The influence of occupational safety and
health on performance with job satisfaction as intervening variables. American
Journal of Economics, special issue, 136-140.
121. Cai, Y., Li, Fi., Zhang, J., & Wu, Z. (2018). Occupational health risk assessment in
the electronics industry in China based on the occupational classification method and
EPA model. International Journal of Environmental Research and Public Health, 15,
2061; doi:10.3390/ijerph15102061
122. Zhou, Z., Miang, Y., Goh, Y. M., & Li, Q. (2015). Overview and analysis of safety
management studies in the construction industry. Safety Science, 72, 337-350.
123. de Castro, A. B., Gee, G. C., & Takeuchi, D. (2008). Relationship between job
dissatisfaction and physical and psychological health among Filipino
immigrants. AAOHN journal: Official journal of the American Association of
Occupational Health Nurses, 56(1), 33–40.
124. Shin, Y., Hur, W. M., Moon, T. W., & Lee, S. (2019). A Motivational Perspective on
Job Insecurity: Relationships Between Job Insecurity, Intrinsic Motivation, and
Performance and Behavioral Outcomes. International journal of environmental
research and public health, 16(10), 1812. doi:10.3390/ijerph16101812
118
Annexure 1 Questionnaire
Effect of Occupational Health, Safety and Welfare Measures on Employee Performance with
Mediation of Job Satisfaction
(A Survey of Sugar Mills employees in KP, Pakistan)
Dear Respondent!
This questionnaire is described to study workers’ performance as a function of occupational
health & Safety and satisfaction from job. The information you provide will help us better
understand this. It is purely for ‘Academic’ purpose. Your cooperation will help the ‘Student-
Scholar’ to fulfill the requirements for PhD in Public Administration. I request you to
respond to all of the questions frankly & honestly. Your responses will be kept strictly
confidential as ID no. instead of your name is kept on each questionnaire. Results of this
survey will be shared with you. Thanks to you & your factory management for co-operation
& time.
Iftikhar Ahmad Khan
Candidate for PhD in Public Administration
DPA, GU, DIK, KP, Pakistan.
119
PERSONAL PROFILE
1. Age: 19-40 years/ 41-60 years
2. Residence: Urban/ Rural
3. Education: up to 5 years/ 6-10 years/ > 10 years
4. Experience: up to 5 years/ > 5 years
Please circle the score which most closely corresponds with how you see the following items:
Strongly
Disagree
Moderately
disagree
disagree Neutral Agree Moderately
agree
Strongly
agree
1 2 3 4 5 6 7
S.
No. Variables & Items SDA MDA DA N A MA SA
Health measures 1 2 3 4 5 6 7
1 Proper healthcare services are
available 1 2 3 4 5 6 7
2 Loud noise is too irritating 1 2 3 4 5 6 7
3 Health education against hazards is
provided 1 2 3 4 5 6 7
4 Periodic annual check-up is conducted 1 2 3 4 5 6 7
5 The atmosphere is clear of dust 1 2 3 4 5 6 7
6 Recreation facilities are available
Safety measures 1 2 3 4 5 6 7
7 Ambulance facilities are available 1 2 3 4 5 6 7
8 Workplace is in order to prevent trips &
falls 1 2 3 4 5 6 7
9 Workers use safety equipment (PPEs) 1 2 3 4 5 6 7
10 I am properly trained to handle
dangerous machinery 1 2 3 4 5 6 7
11 Proper first aid facilities are available 1 2 3 4 5 6 7
Welfare measures 1 2 3 4 5 6 7
12 All basic amenities at home are
available 1 2 3 4 5 6 7
13 Latrines and urinals are available 1 2 3 4 5 6 7
14 Proper work hours are ensured 1 2 3 4 5 6 7
15 Education facilities are available to my
kids 1 2 3 4 5 6 7
16 Benefits of sickness, disablement,
rehabilitation & retirement are given 1 2 3 4 5 6 7
17 Transport facilities are available to my
family
Job satisfaction 1 2 3 4 5 6 7
120
a. Pay 1 2 3 4 5 6 7
18 My pay matches with the work I do 1 2 3 4 5 6 7
19 I am satisfied with other financial
incentives 1 2 3 4 5 6 7
b. Promotion 1 2 3 4 5 6 7
20 I have fair promotion chances 1 2 3 4 5 6 7
21 My performance is evaluated fairly 1 2 3 4 5 6 7
c. Supervision 1 2 3 4 5 6 7
22 My supervisor has caring attitude 1 2 3 4 5 6 7
23 My supervisor guides me in work 1 2 3 4 5 6 7
d. Colleagues 1 2 3 4 5 6 7
24 My colleagues like me 1 2 3 4 5 6 7
25 My co-workers have a sharing attitude 1 2 3 4 5 6 7
e. Work 1 2 3 4 5 6 7
26 I like the work I am supposed to do 1 2 3 4 5 6 7
27 I am too overworked 1 2 3 4 5 6 7
f. Work environment 1 2 3 4 5 6 7
28 I am satisfied with the factory policies 1 2 3 4 5 6 7
29 My objectives align with those of the
factory 1 2 3 4 5 6 7
Employee Performance 1 2 3 4 5 6 7
a. Efficiency 1 2 3 4 5 6 7
30 Quantity of production in factory is
satisfactory 1 2 3 4 5 6 7
31 Resources are spent properly 1 2 3 4 5 6 7
b. Effectiveness 1 2 3 4 5 6 7
32 Quality of product is satisfactory 1 2 3 4 5 6 7
33 Quality of work in factory is satisfactory 1 2 3 4 5 6 7
c. Responsiveness 1 2 3 4 5 6 7
34 Factory owner satisfaction is considered
important by the workers 1 2 3 4 5 6 7
35 Work demands by my supervisory staff
are properly responded 1 2 3 4 5 6 7
d. Innovativeness 1 2 3 4 5 6 7
36 New technological methods in work are
welcomed 1 2 3 4 5 6 7
37 I fully accept new ideas by the
management 1 2 3 4 5 6 7
38
Workers constantly improve their
services as per the changing
requirements of the market
1 2 3 4 5 6 7
